Extended Object Tracking

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developed with YouTube

Email this Page Play all in Full Screen Show More Related Videos

Circular extended object tracking with the Particle Filter

This video illustrates the performance of the Sequential Importance Resampling (SIR) Particle Filter (PF) and the Border Parameterized (BP) PF for the tracking of a circular extended object developed at the University of Sheffield, UK. This is based on data obtained from Fraunhofer FKIE, Germany. The measurement devices are positioned at three key locations, marked with crossed squares, in a curved corridor. Both particle filters estimate the centre position and radius of the extended target based on all the measurements received. The full algorithm is described in the paper: Box Particle / Particle Filtering for State and Parameter Estimation of Extended Objects (currently under review). This work is part of the EU Tracking in Complex Sensor Systems (TRAX) project (Grant agreement no.: 60...

published: 20 Feb 2015
Circular extended object tracking with the box particle filter

This video illustrates the performance of the box particle filter for the tracking of an extended target developed at the University of Sheffield, UK. This is based on data obtained from Fraunhofer FKIE, Germany. The measurement devices are positioned at three key locations, marked with crossed squares, in a curved corridor. The tracking of a single person holding a cylindrical object with radius of 18cm around his body at the height of the sensors is presented in this video clip. The box particle filter estimates the centre position of the person and the radius of the cylindrical object based on all the measurements received. The full algorithm is described in the paper: Box Particle / Particle Filtering for State and Parameter Estimation of Extended Objects (currently under review). This...

published: 04 Feb 2015
Augmented Reality Vuforia Extended Tracking Keep Object Even The Target Lost

Augmented Reality tutorial Keep the object even the target lost with extended tracking

published: 23 Sep 2017
Object Tracking with Sensor Fusion-based Extended Kalman Filter

In this demo, the blue car is the object to be tracked. We continuously got both Lidar (red) and Radar (blue) measurements of the car's location in the defined coordinate, and then we use Extended Kalman Filter to compute the estimated location (green triangle) of the blue car. The estimated trajectory (green triangle) is compared with the ground true trajectory of the blue car, and the error is displayed in RMSE format in real time. The objects to be tracked can be pedestrian, vehicles, or other moving objects around your autonomous car. With Lidar and radar sensors, your autonomous car can measure the locations of the tracked objects. But there might be errors in the sensor data, can we need to combine the two types of measurements to estimate the proper location of the object. Therefor...

published: 02 May 2017
Directional Moving Object Tracking in 2D with the Extended Kalman Filter on Matrix Lie Groups

The moving loudspeaker is tracked with a microphone array. The reference ground truth is obtained with the motion capture system.

published: 22 Sep 2016
Sensor Fusion for Object Tracking

Tracking in modern commercial VR systems is based on the principle of sensor fusion, where measurements from multiple independent sensors are combined to estimate the position and orientation of tracked objects with better quality than what can be achieved by using those same sensors in isolation. This video shows a simulation of a moving and rotating object in two dimensions, tracked by an external absolute measurement system and a relative measurement system integrated into the tracked object. Measurements from these two systems are combined using a non-linear extension of the Kalman filter, yielding a result with low noise, low update latency, and no drift. Related videos: Pure IMU-based Positional Tracking is a No-go: https://www.youtube.com/watch?v=_q_8d0E3tDk Optical 3D Pose Estima ...

published: 02 Sep 2017
Extended Kalman Filter for object tracking

My solution to Udacity Self Driving Car Engineer programme's Extended Kalman Filter project. Blue circles represent laser measurements, red circles radio measurements, green markers are location estimates based on Extended Kalman Filter.

published: 24 May 2017
Object tracking using an extended kalman filter (EKF) with Lidar and Radar measurements

The objective of this project was to track the position of a cyclist using noisy Lidar and Radar sensor measurements. The two separate projects used two different Kalman filter methods to produce the same result. The Extended Kalman Filter, (EKF) uses a standard Kalman filter for sensors providing cartesian coordinate measurements, such as Lidar, and a Jacobian matrix update for sensors providing polar coordinate measurements such as Radar. The red and blue circles indicate Lidar and Radar sensor measurements and the green triangles show the Kalman filters predicted measurement. To learn more about the project, visit the project's website at; "https://www.haidynmcleod.com/extended-kalman-filter"

published: 30 Jul 2017
[Extended Demo] Robust 3D Object Trackinf fro Monocular Images using Stable Parts

This is a demonstration of the 3D pose tracker developed at EPFL CVLab. The method is an extension of the original tracker [1], which gets help from a SLAM method to fill in the detection gaps. We even have an newer version that is even faster and more robust that the one shown here! See project webpage [2] for more details [1] Alberto Crivellaro, Mahdi Rad, Yannick Verdie, Kwang Moo Yi, Pascal Fua, and Vincent Lepetit, "Robust 3D Object Tracking fro Monocular Images using Stable Parts", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 [2] http://cvlab.epfl.ch/research/3d_part_based_tracking

published: 30 Jun 2017
Real-time tracking occluded object using CamShift and Kalman Filter algorithms.

All algorithms are implemented by using c++ without OpenCV library.

published: 26 Jan 2017
3D Object Tracking for Augmented Reality | Srushti Labs

3D Object Tracking for Augmented Reality: We created a prototype for AR - Object Tracking. This method tracks the moving objects with assigned 2d/3d animated content. It also replaces the existing object with assigned 3d prop in real time. Object Tracking is recommended for consumer oriented promotions, events and product launches.

published: 21 Nov 2016
Multiple objects tracking in the presence of long term occlusions

We present a robust object tracking algorithm that handles spatially extended and temporally long object occlusions. The proposed approach is based on the concept of ``object permanence'' which suggests that a totally occluded object will re-emerge near its occluder. The proposed method does not require prior training to account for differences in the shape, size, color or motion of the objects to be tracked. Instead, the method automatically and dynamically builds appropriate object representations that enable robust and effective tracking and occlusion reasoning. The proposed approach has been evaluated on several image sequences showing either complex object manipulation tasks or human activity in the context of surveillance applications. Experimental results demonstrate that the develo...

published: 25 Nov 2010
Model Targets - Vuforia's latest object recognition technology

Model Targets represent the most recent advancement in Vuforia object recognition technology, allowing for the detection and tracking of objects from 3D models. View the original here: https://youtu.be/y70yStPCBHA

published: 26 Jun 2017
Motion-based Object Detection and Tracking Using 3D-LIDAR

Detection and Tracking of Moving Objects Using 2.5D Motion Grids A. Asvadi, P. Peixoto, and U. Nunes, “Detection and Tracking of Moving Objects Using 2.5D Motion Grids,” In IEEE 18th International Conference on Intelligent Transportation Systems (ITSC 2015), pp. 788 – 793, Las Palmas, Spain, 2015. DOI: 10.1109/ITSC.2015.133

published: 29 May 2016
Laser Radar Object Tracking

Udacity Extended Kalman Filter

published: 24 Dec 2017
Object tracking with Sensor Fusion-based Extended Kalman Filter

In this demo, the blue car is the object to be tracked, but the tracked object can be any types, e.g. pedestrian, vehicles, or other moving objects. There are two types of senosr data, LIDAR (red circle) and RADAR (blue circle) measurements of the tracked car's location in the defined coordinate. But there might be noise and errors in the data. Also, we need to find a way to fuse the two types of sensor measurements to estimate the proper location of the tracked object. Therefore, we use Extended Kalman Filter to compute the estimated location (green triangle) of the blue car. The estimated trajectory (green triangle) is compared with the ground true trajectory of the blue car, and the error is displayed in RMSE format in real time. In autonomous driving case, the self-driving cars obtia...

published: 03 May 2017
object tracking using Kalman filter

fall EEL 6562 image processing UFL ECE Ruizhi Li

published: 11 Dec 2013
Visual Object Tracking using Powell's Direct Set Method and Kalman Filtering

This video contains the results of a tracking algorithm which I have proposed in my thesis for MS degree at Military College of Signals, NUST, Pakistan. Thesis Abstract: Visual object tracking is defined as the task of locating an object as it moves around in a video sequence. It has widespread applications in the area of human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control and medical imaging. Amongst all the trackers, kernel based trackers have gained popularity in the recent past because of their simplicity and robustness to track a variety of objects. However, such trackers usually encode only single view of the object and face problems due to changing appearance patterns of the object, non-rigid object structu...

published: 23 Jan 2013
Kudan : Extended Tracking and Detection Feature

published: 25 Nov 2016
Vuforia 3d Object Tracking Test

https://twitter.com/yuujii/status/769547566635560960 === https://twitter.com/yuujii http://2vr.jp HoloLens Apps - Artgram https://www.microsoft.com/store/p/artgram/9nblggh4scc4 - Artgram Unity-Chan https://www.microsoft.com/store/p/artgram-unity-chan/9nblggh4sp8j - HoloExploded https://www.microsoft.com/store/p/holoexploded/9nblggh4s91j

published: 28 Aug 2016
Object tracking with 2D Kalman Filter part 1: Matlab implimentation by Student Dave

Tutorial on how to tracking an object in a image using the 2-d kalman filter! matlab code and more can be found here! http://studentdavestutorials.weebly.com/ if you like those bugs i'm using, check em out here http://www.hexbug.com/nano/

published: 19 Dec 2012
Combining Shape-Changing Interfaces and Spatial Augmented Reality Enables Extended Object Appearance

Combining Shape-Changing Interfaces and Spatial Augmented Reality Enables Extended Object Appearance David Lindlbauer, Jens Emil Groenbaek, Morten Henriksen Birk, Kim Halskov, Marc Alexa, Joerg Mueller Abstract: We propose combining shape-changing interfaces and spatial augmented reality for extending the space of appearances and interactions of actuated interfaces. While shape-changing interfaces can dynamically alter the physical appearance of objects, the integration of spatial augmented reality additionally allows for dynamically changing objects' optical appearance with high detail. This way, devices can render currently challenging features such as high frequency texture or fast motion. We frame this combination in the context of computer graphics with analogies to established techn...

published: 25 Apr 2016
Synthetic Aperture Tracking: Tracking through Occlusions

Occlusion is a significant challenge for many tracking algorithms. Most current methods can track through transient occlusion, but cannot handle significant extended occlusion when the object's trajectory may change significantly. We present a method to track a 3D object through significant occlusion using multiple nearby cameras (e.g., a camera array). When an occluder and object are at different depths, different parts of the object are visible or occluded in each view due to parallax. By aggregating across these views, the method can track even when any individual camera observes very little of the target object. Implementation- wise, the methods are straightforward and build upon established single-camera algorithms. They do not require explicit modeling or reconstruction of the scene ...

published: 30 Dec 2016
vuforia 3d object tracking

published: 03 May 2017

developed with YouTube

1:33

Circular extended object tracking with the Particle Filter

Order: Reorder
Duration: 1:33
Updated: 20 Feb 2015
views: 171

videos

This video illustrates the performance of the Sequential Importance Resampling (SIR) Particle Filter (PF) and the Border Parameterized (BP) PF for the tracking of a circular extended object developed at the University of Sheffield, UK. This is based on data obtained from Fraunhofer FKIE, Germany. The measurement devices are positioned at three key locations, marked with crossed squares, in a curved corridor. Both particle filters estimate the centre position and radius of the extended target based on all the measurements received. The full algorithm is described in the paper: Box Particle / Particle Filtering for State and Parameter Estimation of Extended Objects (currently under review). This work is part of the EU Tracking in Complex Sensor Systems (TRAX) project (Grant agreement no.: 607400) (https://www.trax.utwente.nl/).

https://wn.com/Circular_Extended_Object_Tracking_With_The_Particle_Filter

3:06

Circular extended object tracking with the box particle filter

Order: Reorder
Duration: 3:06
Updated: 04 Feb 2015
views: 112

videos

This video illustrates the performance of the box particle filter for the tracking of an extended target developed at the University of Sheffield, UK. This is based on data obtained from Fraunhofer FKIE, Germany. The measurement devices are positioned at three key locations, marked with crossed squares, in a curved corridor. The tracking of a single person holding a cylindrical object with radius of 18cm around his body at the height of the sensors is presented in this video clip. The box particle filter estimates the centre position of the person and the radius of the cylindrical object based on all the measurements received. The full algorithm is described in the paper: Box Particle / Particle Filtering for State and Parameter Estimation of Extended Objects (currently under review). This work is part of the EU Tracking in Complex Sensor Systems (TRAX) project (Grant agreement no.: 607400) (https://www.trax.utwente.nl/).

https://wn.com/Circular_Extended_Object_Tracking_With_The_Box_Particle_Filter

4:15

Augmented Reality Vuforia Extended Tracking Keep Object Even The Target Lost

Order: Reorder
Duration: 4:15
Updated: 23 Sep 2017
views: 2074

videos

Augmented Reality tutorial Keep the object even the target lost with extended tracking

https://wn.com/Augmented_Reality_Vuforia_Extended_Tracking_Keep_Object_Even_The_Target_Lost

0:48

Object Tracking with Sensor Fusion-based Extended Kalman Filter

Order: Reorder
Duration: 0:48
Updated: 02 May 2017
views: 897

videos

In this demo, the blue car is the object to be tracked. We continuously got both Lidar (red) and Radar (blue) measurements of the car's location in the defined coordinate, and then we use Extended Kalman Filter to compute the estimated location (green triangle) of the blue car. The estimated trajectory (green triangle) is compared with the ground true trajectory of the blue car, and the error is displayed in RMSE format in real time. The objects to be tracked can be pedestrian, vehicles, or other moving objects around your autonomous car. With Lidar and radar sensors, your autonomous car can measure the locations of the tracked objects. But there might be errors in the sensor data, can we need to combine the two types of measurements to estimate the proper location of the object. Therefore, we apply the Extended Kalman Filter to track the objects based on fused sensor data. Source code: https://github.com/JunshengFu/Tracking-with-Extended-Kalman-Filter

https://wn.com/Object_Tracking_With_Sensor_Fusion_Based_Extended_Kalman_Filter

2:37

Directional Moving Object Tracking in 2D with the Extended Kalman Filter on Matrix Lie Groups

Order: Reorder
Duration: 2:37
Updated: 22 Sep 2016
views: 151

videos

The moving loudspeaker is tracked with a microphone array. The reference ground truth is obtained with the motion capture system.

https://wn.com/Directional_Moving_Object_Tracking_In_2D_With_The_Extended_Kalman_Filter_On_Matrix_Lie_Groups

24:56

Sensor Fusion for Object Tracking

Order: Reorder
Duration: 24:56
Updated: 02 Sep 2017
views: 6027

videos

Tracking in modern commercial VR systems is based on the principle of sensor fusion, where measurements from multiple independent sensors are combined to estimate the position and orientation of tracked objects with better quality than what can be achieved by using those same sensors in isolation. This video shows a simulation of a moving and rotating object in two dimensions, tracked by an external absolute measurement system and a relative measurement system integrated into the tracked object. Measurements from these two systems are combined using a non-linear extension of the Kalman filter, yielding a result with low noise, low update latency, and no drift. Related videos: Pure IMU-based Positional Tracking is a No-go: https://www.youtube.com/watch?v=_q_8d0E3tDk Optical 3D Pose Estimation of Oculus Rift DK2: https://www.youtube.com/watch?v=X4G6_zt1qKY Lighthouse Tracking Examined - Headset at Rest: https://www.youtube.com/watch?v=Uzv2H3PDPDg Lighthouse Tracking Examined - Headset in Motion: https://www.youtube.com/watch?v=XwxwMruEE7Y Lighthouse Tracking Examined - Controller in Motion: https://www.youtube.com/watch?v=A75uKqA67FI Playstation Move Tracking Test: https://www.youtube.com/watch?v=0J5LaWykiIU More information: https://en.wikipedia.org/wiki/Kalman_filter

https://wn.com/Sensor_Fusion_For_Object_Tracking

0:36

Extended Kalman Filter for object tracking

Order: Reorder
Duration: 0:36
Updated: 24 May 2017
views: 50

videos

My solution to Udacity Self Driving Car Engineer programme's Extended Kalman Filter project. Blue circles represent laser measurements, red circles radio measurements, green markers are location estimates based on Extended Kalman Filter.

https://wn.com/Extended_Kalman_Filter_For_Object_Tracking

0:12

Object tracking using an extended kalman filter (EKF) with Lidar and Radar measurements

Order: Reorder
Duration: 0:12
Updated: 30 Jul 2017
views: 38

videos

The objective of this project was to track the position of a cyclist using noisy Lidar and Radar sensor measurements. The two separate projects used two different Kalman filter methods to produce the same result. The Extended Kalman Filter, (EKF) uses a standard Kalman filter for sensors providing cartesian coordinate measurements, such as Lidar, and a Jacobian matrix update for sensors providing polar coordinate measurements such as Radar. The red and blue circles indicate Lidar and Radar sensor measurements and the green triangles show the Kalman filters predicted measurement. To learn more about the project, visit the project's website at; "https://www.haidynmcleod.com/extended-kalman-filter"

https://wn.com/Object_Tracking_Using_An_Extended_Kalman_Filter_(Ekf)_With_Lidar_And_Radar_Measurements

1:00

[Extended Demo] Robust 3D Object Trackinf fro Monocular Images using Stable Parts

Order: Reorder
Duration: 1:00
Updated: 30 Jun 2017
views: 326

videos

This is a demonstration of the 3D pose tracker developed at EPFL CVLab. The method is an extension of the original tracker [1], which gets help from a SLAM method to fill in the detection gaps. We even have an newer version that is even faster and more robust that the one shown here! See project webpage [2] for more details [1] Alberto Crivellaro, Mahdi Rad, Yannick Verdie, Kwang Moo Yi, Pascal Fua, and Vincent Lepetit, "Robust 3D Object Tracking fro Monocular Images using Stable Parts", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 [2] http://cvlab.epfl.ch/research/3d_part_based_tracking

https://wn.com/Extended_Demo_Robust_3D_Object_Trackinf_Fro_Monocular_Images_Using_Stable_Parts

0:21

Real-time tracking occluded object using CamShift and Kalman Filter algorithms.

Order: Reorder
Duration: 0:21
Updated: 26 Jan 2017
views: 205

videos

All algorithms are implemented by using c++ without OpenCV library.

https://wn.com/Real_Time_Tracking_Occluded_Object_Using_Camshift_And_Kalman_Filter_Algorithms.

1:07

3D Object Tracking for Augmented Reality | Srushti Labs

Order: Reorder
Duration: 1:07
Updated: 21 Nov 2016
views: 4138

videos

3D Object Tracking for Augmented Reality: We created a prototype for AR - Object Tracking. This method tracks the moving objects with assigned 2d/3d animated content. It also replaces the existing object with assigned 3d prop in real time. Object Tracking is recommended for consumer oriented promotions, events and product launches.

https://wn.com/3D_Object_Tracking_For_Augmented_Reality_|_Srushti_Labs

2:39

Multiple objects tracking in the presence of long term occlusions

Order: Reorder
Duration: 2:39
Updated: 25 Nov 2010
views: 25747

videos

We present a robust object tracking algorithm that handles spatially extended and temporally long object occlusions. The proposed approach is based on the concept of ``object permanence'' which suggests that a totally occluded object will re-emerge near its occluder. The proposed method does not require prior training to account for differences in the shape, size, color or motion of the objects to be tracked. Instead, the method automatically and dynamically builds appropriate object representations that enable robust and effective tracking and occlusion reasoning. The proposed approach has been evaluated on several image sequences showing either complex object manipulation tasks or human activity in the context of surveillance applications. Experimental results demonstrate that the developed tracker is capable of handling several challenging situations, where the labels of objects are correctly identified and maintained over time, despite the complex interactions among the tracked objects that lead to several layers of occlusions. For more details see: http://www.ics.forth.gr/~argyros/research/occlusions.html Reference: V. Papadourakis, A.A. Argyros, "Multiple Objects Tracking in the Presence of Long-term Occlusions", in Computer Vision and Image Understanding, Elsevier, vol. 114, issue 7, pp. 835-846, July 2010.

https://wn.com/Multiple_Objects_Tracking_In_The_Presence_Of_Long_Term_Occlusions

0:29

Model Targets - Vuforia's latest object recognition technology

Order: Reorder
Duration: 0:29
Updated: 26 Jun 2017
views: 24043

videos

Model Targets represent the most recent advancement in Vuforia object recognition technology, allowing for the detection and tracking of objects from 3D models. View the original here: https://youtu.be/y70yStPCBHA

https://wn.com/Model_Targets_Vuforia's_Latest_Object_Recognition_Technology

0:23

Motion-based Object Detection and Tracking Using 3D-LIDAR

Order: Reorder
Duration: 0:23
Updated: 29 May 2016
views: 1350

videos

Detection and Tracking of Moving Objects Using 2.5D Motion Grids A. Asvadi, P. Peixoto, and U. Nunes, “Detection and Tracking of Moving Objects Using 2.5D Motion Grids,” In IEEE 18th International Conference on Intelligent Transportation Systems (ITSC 2015), pp. 788 – 793, Las Palmas, Spain, 2015. DOI: 10.1109/ITSC.2015.133

https://wn.com/Motion_Based_Object_Detection_And_Tracking_Using_3D_Lidar

0:16

Laser Radar Object Tracking

Order: Reorder
Duration: 0:16
Updated: 24 Dec 2017
views: 10

videos

Udacity Extended Kalman Filter

https://wn.com/Laser_Radar_Object_Tracking

0:20

Object tracking with Sensor Fusion-based Extended Kalman Filter

Order: Reorder
Duration: 0:20
Updated: 03 May 2017
views: 251

videos

In this demo, the blue car is the object to be tracked, but the tracked object can be any types, e.g. pedestrian, vehicles, or other moving objects. There are two types of senosr data, LIDAR (red circle) and RADAR (blue circle) measurements of the tracked car's location in the defined coordinate. But there might be noise and errors in the data. Also, we need to find a way to fuse the two types of sensor measurements to estimate the proper location of the tracked object. Therefore, we use Extended Kalman Filter to compute the estimated location (green triangle) of the blue car. The estimated trajectory (green triangle) is compared with the ground true trajectory of the blue car, and the error is displayed in RMSE format in real time. In autonomous driving case, the self-driving cars obtian both Lidar and radar sensors measurements of objects to be tracked, and then apply the Extended Kalman Filter to track the objects based on the two types of sensor data. In the video, we compare ground true with three other tracking cases: only with lidar, only with radar, and with both lidar and radar. Source code: https://github.com/JunshengFu/Tracking-with-Extended-Kalman-Filter

https://wn.com/Object_Tracking_With_Sensor_Fusion_Based_Extended_Kalman_Filter

10:26

object tracking using Kalman filter

Order: Reorder
Duration: 10:26
Updated: 11 Dec 2013
views: 14090

videos

fall EEL 6562 image processing UFL ECE Ruizhi Li

https://wn.com/Object_Tracking_Using_Kalman_Filter

2:47

Visual Object Tracking using Powell's Direct Set Method and Kalman Filtering

Order: Reorder
Duration: 2:47
Updated: 23 Jan 2013
views: 7905

videos

This video contains the results of a tracking algorithm which I have proposed in my thesis for MS degree at Military College of Signals, NUST, Pakistan. Thesis Abstract: Visual object tracking is defined as the task of locating an object as it moves around in a video sequence. It has widespread applications in the area of human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control and medical imaging. Amongst all the trackers, kernel based trackers have gained popularity in the recent past because of their simplicity and robustness to track a variety of objects. However, such trackers usually encode only single view of the object and face problems due to changing appearance patterns of the object, non-rigid object structures, object-to-object and object-to-scene occlusions, and camera motion. In this research, a new kernel-based method for real-time tracking of objects seen from a moving or static camera is proposed with an object to resolve these problems. In contrast to brute-force search, this method uses Powell's gradient ascent method to optimally find the most likely target position in every frame. Moreover, a template adaption module has also been proposed which accounts for the changes in shape, size, orientation and shading conditions of the target object over time. The proposed algorithm also handles short-term partial and full occlusion by using Kalman filter for trajectory prediction and Proximity Search for relocking object once it reappears in the scene after occlusion. The performance of proposed algorithm has been evaluated on a number of publicly available real-world sequences. Experimental results show robust performance of tracker for objects with changing appearance and undergoing short-term and long-term full occlusion. The computational complexity of the tracker is exceptionally low, thus making it suitable for real-time applications.

https://wn.com/Visual_Object_Tracking_Using_Powell's_Direct_Set_Method_And_Kalman_Filtering

1:29

Kudan : Extended Tracking and Detection Feature

Order: Reorder
Duration: 1:29
Updated: 25 Nov 2016
views: 4198

videos

https://wn.com/Kudan_Extended_Tracking_And_Detection_Feature

0:29

Vuforia 3d Object Tracking Test

Order: Reorder
Duration: 0:29
Updated: 28 Aug 2016
views: 2142

videos

https://twitter.com/yuujii/status/769547566635560960 === https://twitter.com/yuujii http://2vr.jp HoloLens Apps - Artgram https://www.microsoft.com/store/p/artgram/9nblggh4scc4 - Artgram Unity-Chan https://www.microsoft.com/store/p/artgram-unity-chan/9nblggh4sp8j - HoloExploded https://www.microsoft.com/store/p/holoexploded/9nblggh4s91j

https://wn.com/Vuforia_3D_Object_Tracking_Test

11:49

Object tracking with 2D Kalman Filter part 1: Matlab implimentation by Student Dave

Order: Reorder
Duration: 11:49
Updated: 19 Dec 2012
views: 45043

videos

Tutorial on how to tracking an object in a image using the 2-d kalman filter! matlab code and more can be found here! http://studentdavestutorials.weebly.com/ if you like those bugs i'm using, check em out here http://www.hexbug.com/nano/

https://wn.com/Object_Tracking_With_2D_Kalman_Filter_Part_1_Matlab_Implimentation_By_Student_Dave

0:31

Combining Shape-Changing Interfaces and Spatial Augmented Reality Enables Extended Object Appearance

Order: Reorder
Duration: 0:31
Updated: 25 Apr 2016
views: 1109

videos

Combining Shape-Changing Interfaces and Spatial Augmented Reality Enables Extended Object Appearance David Lindlbauer, Jens Emil Groenbaek, Morten Henriksen Birk, Kim Halskov, Marc Alexa, Joerg Mueller Abstract: We propose combining shape-changing interfaces and spatial augmented reality for extending the space of appearances and interactions of actuated interfaces. While shape-changing interfaces can dynamically alter the physical appearance of objects, the integration of spatial augmented reality additionally allows for dynamically changing objects' optical appearance with high detail. This way, devices can render currently challenging features such as high frequency texture or fast motion. We frame this combination in the context of computer graphics with analogies to established techniques for increasing the realism of 3D objects such as bump mapping. This extensible framework helps us identify challenges of the two techniques and benefits of their combination. We utilize our prototype shape-changing device enriched with spatial augmented reality through projection mapping to demonstrate the concept. We present a novel mechanical distance-fields algorithm for real-time fitting of mechanically constrained shape-changing devices to arbitrary 3D graphics. Furthermore, we present a technique for increasing effective screen real estate for spatial augmented reality through view-dependent shape change. ACM DL: http://dl.acm.org/citation.cfm?id=2858457 DOI: http://dx.doi.org/10.1145/2858036.2858457 ------ https://chi2016.acm.org/wp/

https://wn.com/Combining_Shape_Changing_Interfaces_And_Spatial_Augmented_Reality_Enables_Extended_Object_Appearance

4:46

Synthetic Aperture Tracking: Tracking through Occlusions

Order: Reorder
Duration: 4:46
Updated: 30 Dec 2016
views: 422

videos

Occlusion is a significant challenge for many tracking algorithms. Most current methods can track through transient occlusion, but cannot handle significant extended occlusion when the object's trajectory may change significantly. We present a method to track a 3D object through significant occlusion using multiple nearby cameras (e.g., a camera array). When an occluder and object are at different depths, different parts of the object are visible or occluded in each view due to parallax. By aggregating across these views, the method can track even when any individual camera observes very little of the target object. Implementation- wise, the methods are straightforward and build upon established single-camera algorithms. They do not require explicit modeling or reconstruction of the scene and enable tracking in complex, dynamic scenes with moving cameras. Analysis of accuracy and robustness shows that these methods are successful when upwards of '70% of the object is occluded in every camera view. To the best of our knowledge, this system is the first capable of tracking in the presence of such significant occlusion.

https://wn.com/Synthetic_Aperture_Tracking_Tracking_Through_Occlusions

1:26

vuforia 3d object tracking

Order: Reorder
Duration: 1:26
Updated: 03 May 2017
views: 2971

videos

https://wn.com/Vuforia_3D_Object_Tracking

Email this Page Play all in Full Screen Show More Related Videos

Sensor Fusion for Object Tracking

Tracking in modern commercial VR systems is based on the principle of sensor fusion, where measurements from multiple independent sensors are combined to estimate the position and orientation of tracked objects with better quality than what can be achieved by using those same sensors in isolation. This video shows a simulation of a moving and rotating object in two dimensions, tracked by an external absolute measurement system and a relative measurement system integrated into the tracked object. Measurements from these two systems are combined using a non-linear extension of the Kalman filter, yielding a result with low noise, low update latency, and no drift. Related videos: Pure IMU-based Positional Tracking is a No-go: https://www.youtube.com/watch?v=_q_8d0E3tDk Optical 3D Pose Estima ...

published: 02 Sep 2017
Harald Wuest (VisionLib): Challenges for Tracking Technologies in Industrial AR Applications

A talk from the Develop Track at AWE USA 2017 - the largest conference for AR+VR in Santa Clara, California May 31- June 2, 2017. Harald Wuest (VisionLib): Challenges for Tracking Technologies in Industrial AR Applications Varying lighting conditions, reflections and dynamic scene changes make the visual tracking of objects in industrial augmented reality scenarios a challenging task. This talk gives an overview of suitable tracking technologies for different use cases in the industrial domain, which address these typical problems. We present the benefits of using CAD models both for object tracking methods and also for an easy setup process. Showcasing a couple of service and maintenance applications, we demonstrate the performance and the power of model-based tracking techniques and the...

published: 09 Jun 2017
Shariq Iqbal, Jeff MacInnes | Dynamic Object Gaze Tracking with OpenCV

PyData Carolinas 2016 Using computer vision techniques, we extended eye tracking technology to allow for data normalization across dynamic environments. We applied these techniques to subjects viewing artwork at Duke’s Nasher Museum of Art. Most eye tracking solutions track gaze with respect to a static object like a computer screen, making it easy to know exactly where a person is looking with respect to an image on the screen. This makes analysis easier, but doesn’t accurately reflect real-life. What happens when we move eye tracking into a more realistic, dynamic setting, using eye tracking glasses that allow people to move around? People can interact with objects in a much more natural manner, but a new challenge is introduced: We only have gaze data with respect to the glasses frame...

published: 06 Oct 2016
3D Object Tracking for Augmented Reality: Handling Multiple Objects, Motion-Blur, & Lack of Texture

Augmented Reality (AR) is implemented by superimposing virtual entities on the real scene so that they appear registered with the related real entities. For the registration to be reliable, qualified object tracking technologies are necessary. In this talk, we address the problems of 3D object tracking in implementing practical AR systems: handling multiple 3D objects, motion blur, and texture-less objects. Presenting the solutions for these problems, we focus on reducing the tracking dependency on the complex texture. Finally, in the texture-less object tracking, we show how to exploit the depth information obtained in real-time using Microsoft Kinect.

published: 27 Jul 2016
Object tracking

Arnold W.M. Smeulders, University of Amsterdam with Dung Chu In contrast, the task of tracking in computer vision, in spite of being the older topic, is believed to be at a younger state of development. This is attributed to the intrinsic difficulty of the task. In our contribution to the topic, we have recently conducted an international experimental review [11] aiming to order and to cover all circumstances a tracking algorithm may encounter. The size of the dataset, the number of trackers, and the evaluation metrics at the group level provide insight what trackers perform well under what circumstances, providing provisional insights what are characteristics of good trackers, and the hardness of the problem.

published: 18 Dec 2013
The LEGO Batman Movie Story Pack - 100% Guide #2 - Fortress of Solitude (All Minikits)

LEGO Dimensions - The LEGO Batman Movie Story Pack 100% guide for all 10 minikits and the person in peril for level 2 - The Fortress of Solitude. Below are the time stamps for each collectible. Please use them to quickly skip to any collectible you are looking for. 02:12 to 02:20 - Minikit #1 - Requires super strength 02:20 to 02:54 - Minikit #2 - Destroy 5 gold objects 02:59 to 03:41 - Minikit #3 - Requires pole vault and grapple abilities 03:53 to 04:13 - Minikit #4 - Requires underwater vehicle grapple 04:15 to 04:34 - Minikit #5 - Requires dive / repair abilities 14:28 to 14:46 - Bonus Room Access - Requires Ethan Hunt 15:10 to 15:25 - Minikit #6 - Requires tracking 15:30 - Menial Task Mike rescued - Destroy silver object 15:44 to 16:22 - Minikit #7 - Requires Abby Yates 16:32 to 1...

published: 14 Feb 2017
Astrophotography without a star tracker

If you don't have a star tracker, here's what I've so far found the best way to take deep space astrophotography photos. You'll do better with a good tracker, but I got some good results without one. http://forrest-tanaka.com/ Exposure information: Focal length: 280mm Aperture: f/4.0 ISO: 6400 Shutter: 1.6s

published: 24 Feb 2012
An Application of Extended Kalman Filter for Spacecraft Orbit Estimation - Akila S

PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical model to generate state estimates. These techniques are essential for numerical weather and climate predictions, but aplications of DA to many other scientific and engineering disciplines are emerging rapidly. DA is inherently interdisciplinary in nature and requires close collaborations and interactions between researchers in atmospheric sciences, in nonlinear dynamics and complex systems, and in applied mathematics and statistics. The Data Assimilation Research Program (DARP) is primarily aimed at develop...

published: 28 May 2013
Kalman Filter Tutorial in MATLAB

published: 13 Apr 2015
Augmented reality Tutorial No. 27: Unity3D, Vuforia, Zigfu and Kinect - Control Character with Body

We share the knowledge. And you? Hit like button and share with everyone! More info on this Augmented Reality tutorial: http://www.ourtechart.com/augmented-reality/augmented-reality-body-tracking-kinect/

published: 23 Jul 2015
In-depth Jungle discussions on Solo-Q&Competitive w/ Cloud9 Wiggily

TIMESTAMPS BELOW This video is extremely long and covers an extremely wide range of topics all pertaining to the jungle. The Jungler who joins me in this video is NA Season 7's Rank 1 player and current Cloud9 professional Jungler Wiggily. 2:40 - Introduction 3:48 - CS Counting 5:15 - Rubberbanding 12:20 - Jungle/Lane Interactions in the West 14:15 - Rubberbanding Effects 15:08 - Rubberbanding Jungle Strategy 19:04 - Jungle Style Without Rubberbanding 20:51 - Season 8 Jungle Prediction -- Domination: 26:48 - Predator 29:35 - Electrocute 32:07 - Dark Harvest -- Precision: 34:29 - Press the Attack 37:26 - Lethal Tempo 38:30 - Fleet Footwork -- 41:15 - Precision Tree + Discussion 44:44 - Domination Tree + Discussion -- Resolve: 50:20 - Guardian 50:40 - Aftershock/Grasp 51:25 - Resolve Tree ...

published: 01 Dec 2017
SynthEyes INTENSE - knowledge for Camera Tracking

This Tutorial was made during my free time. It is non-profit. In this intense 3D Tracking Tutorial I take you through syntheyes powerful Camera Tracking features. final result: http://www.youtube.com/watch?v=MeaFyFXRmGQ ********************************** WHAT YOU WILL LEARN: --1.--We will start by quickly discussing the principles behind camera tracking - Photogrammerty - to make you familar with matchmoving --2.--The following lessons will be spent on syntheyes User Interface and on Workflow optimization. --3.--Jumping right into Target Tracking we will discuss the matchmovers decision on wheather a feature is good to track or not, followed by an intense lesson on supervised Target Tracking with SynthEyes 2008 target functionalitys and the Graph Editor. --4.--We will automize the ...

published: 08 Sep 2010
Introduction to Destruction with Simon Fenton - Escape Studios Webinar

Simon took us through a destructive introduction to Unreal Engine 4 (UE4), demonstrating how to set up a real time fracture of an object and then, blow it up. UE4 is the same games engine that is used in the Gears of War and the Batman games - pretty cool, right? The explosive webinar looked at the physics systems in the graphics power-house that is Unreal Engine. Hit Subscribe for more! Follow us on Twitter! https://twitter.com/Escape_Studios Like us on Facebook! http://www.facebook.com/escapestudios

published: 08 Oct 2015
Augmented Reality Tutorial No. 30: Fusion Effect using MultiTarget Tracking (Unity3D, Vuforia)

We share the knowledge. And you? Hit like button and share with everyone! More info on this Augmented Reality tutorial: http://www.ourtechart.com/augmented-reality/augmented-reality-multitarget-tracking/

published: 04 Aug 2015
Pmomo: Projection Mapping on Movable 3D Object

Pmomo: Projection Mapping on Movable 3D Object Yi Zhou, Shuangjiu Xiao, Ning Tang, Zhiyong Wei, Xu Chen CHI '16: ACM Conference on Human Factors in Computing Systems Session: Real Reality Interfaces Abstract We introduce Pmomo (acronym of projection mapping on movable object), a dynamic projection mapping system that tracks the 6-DOF position of real-world object, and shades it with virtual 3D contents by projection. The system can precisely lock the projection on the moving object in real-time, even the one with complex geometry. Based on depth camera, we developed a novel and robust tracking method that samples the structure of the object into low-density point cloud, then performs an adaptive searching scheme for the registration procedure. As a fully interactive system, our method ca...

published: 31 May 2016
Visão Computacional com filtro de Kalman - Object Tracking

Explicação teórica dos principais conceitos envolvidos no projeto de rastreamento de objetos baseado em cor com filtro de Kalman desenvolvido na disciplina de Processamento Digital de Imagens da UTFPR Curitiba. Autor: Marcos Rogério Fernandes 2015.2 Demonstração do projeto em funcionamento: https://www.youtube.com/watch?v=tREbIw9DxHA

published: 07 Jan 2016
Florian Wilhelm - Handling GPS Data with Python

Florian Wilhelm - Handling GPS Data with Python [EuroPython 2016] [22 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/handling-gps-data-with-python) If you have ever happened to need to deal with GPS data in Python you may have felt a bit lost. This talk presents libraries starting from basic reading and writing GPS tracks in the GPS Exchange Format to adding missing elevation information. Also visualisation of tracks on OpenStreetmap data with interactive plots in Jupyter notebooks is covered. Additionally common algorithms for GPS like Douglas-Peucker and Kalman filter are explained. ----- If you have ever happened to need to deal with GPS data in Python you may have felt a bit lost. There are many libraries at various states of maturity and scope....

published: 06 Aug 2016
Floating static route with IP SLA Tracking

Floating static route with IP SLA Tracking فى الشبكات التى لها اكثر من مسار شرح استخدام Wan redundancy on Cisco device Use Static backup route with IP SLA Tracking How determine the state of primary connection without enable dynamic routing How ask the router to do some work on behalf you كيف نطلب من جهاز التوجيه للقيام ببعض الأعمال نيابة عنك http://www.facebook.com/elearning.NetwOrkers Hani Adel Cisco

published: 02 Apr 2012
Syfy UFO Documentary Government Secret Truth Exposed Aliens on the Moon Full HD 2015

Unexplained and Unidentified Objects found one the Moon the Truth hast been Exposed about Aliens in 2015. All Government / NASA UFO secrets and mysteries explained. During the 1950s many U.F.O.S seen over Earth were tracked back to the Moon by government tracking stations in secret complexes in deserts in Arizona and Nevada and inside underground mountain bases. WE HAVE ONE PHOTO OF A SAUCER SHAPED CRAFT HOVERING OVER THE MOON, taken by a civilian astronomer. Sergeant Willard Wannail, who investigated U.F.O. landings on Ohio when in Army Intelligence, showed us an 8 by 10 CLEAR glossy detailed photo of a silvery spaceship hovering directly over a huge Moon landscape, estimated to be several miles long, and said to be CITY-SHIP designed to transport thousands of people between solar systems...

published: 29 Dec 2014
tutorial Unity and vuforia Augmented reality model 3d move forward

To show all videos and download all unity packages for free must be Subscriber youtube channel https://www.youtube.com/channel/UCNJVG9_IebHe-NF-K_Y8Grw?sub_confirmation=1 tutorial Unity : Augmented reality ar vuforia * move forward 3d model by button * how used Extended Tracking to moving

published: 02 Sep 2017

developed with YouTube

24:56

Sensor Fusion for Object Tracking

Order: Reorder
Duration: 24:56
Updated: 02 Sep 2017
views: 6027

videos

https://wn.com/Sensor_Fusion_For_Object_Tracking

30:22

Harald Wuest (VisionLib): Challenges for Tracking Technologies in Industrial AR Applications

Order: Reorder
Duration: 30:22
Updated: 09 Jun 2017
views: 673

videos

A talk from the Develop Track at AWE USA 2017 - the largest conference for AR+VR in Santa Clara, California May 31- June 2, 2017. Harald Wuest (VisionLib): Challenges for Tracking Technologies in Industrial AR Applications Varying lighting conditions, reflections and dynamic scene changes make the visual tracking of objects in industrial augmented reality scenarios a challenging task. This talk gives an overview of suitable tracking technologies for different use cases in the industrial domain, which address these typical problems. We present the benefits of using CAD models both for object tracking methods and also for an easy setup process. Showcasing a couple of service and maintenance applications, we demonstrate the performance and the power of model-based tracking techniques and their enormous potential in the field of industrial augmented reality. http://AugmentedWorldExpo.com Link to Slides: https://www.slideshare.net/AugmentedWorldExpo/harald-wuest-visionlib-challenges-for-tracking-technologies-in-industrial-ar-applications

https://wn.com/Harald_Wuest_(Visionlib)_Challenges_For_Tracking_Technologies_In_Industrial_Ar_Applications

33:28

Shariq Iqbal, Jeff MacInnes | Dynamic Object Gaze Tracking with OpenCV

Order: Reorder
Duration: 33:28
Updated: 06 Oct 2016
views: 358

videos

PyData Carolinas 2016 Using computer vision techniques, we extended eye tracking technology to allow for data normalization across dynamic environments. We applied these techniques to subjects viewing artwork at Duke’s Nasher Museum of Art. Most eye tracking solutions track gaze with respect to a static object like a computer screen, making it easy to know exactly where a person is looking with respect to an image on the screen. This makes analysis easier, but doesn’t accurately reflect real-life. What happens when we move eye tracking into a more realistic, dynamic setting, using eye tracking glasses that allow people to move around? People can interact with objects in a much more natural manner, but a new challenge is introduced: We only have gaze data with respect to the glasses frame of reference. In order to apply conventional analysis methods to these data, we need to map dynamic gaze back onto a static reference image, compensating for distance, head movement, and perspective. Using the OpenCV package and its efficient implementations of common computer vision algorithms, we developed a method to find objects of interest in video from eye tracking glasses and return gaze coordinates over those objects, enabling experimenters to apply conventional data analysis methods to eye tracking behavior obtained in dynamic, real-world situations.

https://wn.com/Shariq_Iqbal,_Jeff_Macinnes_|_Dynamic_Object_Gaze_Tracking_With_Opencv

1:25:01

3D Object Tracking for Augmented Reality: Handling Multiple Objects, Motion-Blur, & Lack of Texture

Order: Reorder
Duration: 1:25:01
Updated: 27 Jul 2016
views: 1598

videos

Augmented Reality (AR) is implemented by superimposing virtual entities on the real scene so that they appear registered with the related real entities. For the registration to be reliable, qualified object tracking technologies are necessary. In this talk, we address the problems of 3D object tracking in implementing practical AR systems: handling multiple 3D objects, motion blur, and texture-less objects. Presenting the solutions for these problems, we focus on reducing the tracking dependency on the complex texture. Finally, in the texture-less object tracking, we show how to exploit the depth information obtained in real-time using Microsoft Kinect.

https://wn.com/3D_Object_Tracking_For_Augmented_Reality_Handling_Multiple_Objects,_Motion_Blur,_Lack_Of_Texture

1:07:03

Object tracking

Order: Reorder
Duration: 1:07:03
Updated: 18 Dec 2013
views: 2707

videos

Arnold W.M. Smeulders, University of Amsterdam with Dung Chu In contrast, the task of tracking in computer vision, in spite of being the older topic, is believed to be at a younger state of development. This is attributed to the intrinsic difficulty of the task. In our contribution to the topic, we have recently conducted an international experimental review [11] aiming to order and to cover all circumstances a tracking algorithm may encounter. The size of the dataset, the number of trackers, and the evaluation metrics at the group level provide insight what trackers perform well under what circumstances, providing provisional insights what are characteristics of good trackers, and the hardness of the problem.

https://wn.com/Object_Tracking

22:03

The LEGO Batman Movie Story Pack - 100% Guide #2 - Fortress of Solitude (All Minikits)

Order: Reorder
Duration: 22:03
Updated: 14 Feb 2017
views: 326326

videos

LEGO Dimensions - The LEGO Batman Movie Story Pack 100% guide for all 10 minikits and the person in peril for level 2 - The Fortress of Solitude. Below are the time stamps for each collectible. Please use them to quickly skip to any collectible you are looking for. 02:12 to 02:20 - Minikit #1 - Requires super strength 02:20 to 02:54 - Minikit #2 - Destroy 5 gold objects 02:59 to 03:41 - Minikit #3 - Requires pole vault and grapple abilities 03:53 to 04:13 - Minikit #4 - Requires underwater vehicle grapple 04:15 to 04:34 - Minikit #5 - Requires dive / repair abilities 14:28 to 14:46 - Bonus Room Access - Requires Ethan Hunt 15:10 to 15:25 - Minikit #6 - Requires tracking 15:30 - Menial Task Mike rescued - Destroy silver object 15:44 to 16:22 - Minikit #7 - Requires Abby Yates 16:32 to 16:41 - Minikit #8 - Requires Spinjitzu 16:55 to 17:14 - Minikit #9 - Requires drill ability 17:33 to 17:54 - Minikit #10 - Requires relic scanner / grapple / gold melt abilities

https://wn.com/The_Lego_Batman_Movie_Story_Pack_100_Guide_2_Fortress_Of_Solitude_(All_Minikits)

24:04

Astrophotography without a star tracker

Order: Reorder
Duration: 24:04
Updated: 24 Feb 2012
views: 308345

videos

If you don't have a star tracker, here's what I've so far found the best way to take deep space astrophotography photos. You'll do better with a good tracker, but I got some good results without one. http://forrest-tanaka.com/ Exposure information: Focal length: 280mm Aperture: f/4.0 ISO: 6400 Shutter: 1.6s

https://wn.com/Astrophotography_Without_A_Star_Tracker

22:02

An Application of Extended Kalman Filter for Spacecraft Orbit Estimation - Akila S

Order: Reorder
Duration: 22:02
Updated: 28 May 2013
views: 1201

videos

PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical model to generate state estimates. These techniques are essential for numerical weather and climate predictions, but aplications of DA to many other scientific and engineering disciplines are emerging rapidly. DA is inherently interdisciplinary in nature and requires close collaborations and interactions between researchers in atmospheric sciences, in nonlinear dynamics and complex systems, and in applied mathematics and statistics. The Data Assimilation Research Program (DARP) is primarily aimed at developing an interest within the academic and scientific community in pure and applied research related to DA, in order to form a group of researchers, developers, and users of data assimilation systems. The initial set of activities will consist of a "Monsoon School on Mathematical and Statistical Foundations of Data Assimilation" along with an "International Conference on Data Assimilation" at the TIFR Centre for Applicable Mathematics and the Indian Institute of Science, Bangalore. The school (04-12 July and 18-23 July) will consist of compact courses, tutorials, and hands-on laboratories on these topics during the first half. The participants will conduct short projects during the second part of the school. The expected participants are students and young researchers, as well as scientists at organizations which are interested in operational DA. The list of topics to be covered in the school is as follows. 1. Introduction -- the need for data assimilation 2. Mathematical and statistical methods for assimilation a. nudging, optimal interpolation b. variational methods c. Kalman filtering and related methods d. Statistical sampling techniques 3. Basic introduction to nonlinear dynamics and data assimilation for nonlinear systems 4. Applications to atmospheric, oceanic problems (including basic introduction to modelling) The conference (13-15 July) will consist of invited and contributed talks during second week of the monsoon school. The list of invited speakers and abstract submission for contributed talks will be announced on the program webpage soon.

https://wn.com/An_Application_Of_Extended_Kalman_Filter_For_Spacecraft_Orbit_Estimation_Akila_S

44:35

Kalman Filter Tutorial in MATLAB

Order: Reorder
Duration: 44:35
Updated: 13 Apr 2015
views: 27597

videos

https://wn.com/Kalman_Filter_Tutorial_In_Matlab

20:21

Augmented reality Tutorial No. 27: Unity3D, Vuforia, Zigfu and Kinect - Control Character with Body

Order: Reorder
Duration: 20:21
Updated: 23 Jul 2015
views: 32920

videos

We share the knowledge. And you? Hit like button and share with everyone! More info on this Augmented Reality tutorial: http://www.ourtechart.com/augmented-reality/augmented-reality-body-tracking-kinect/

https://wn.com/Augmented_Reality_Tutorial_No._27_Unity3D,_Vuforia,_Zigfu_And_Kinect_Control_Character_With_Body

3:41:56

In-depth Jungle discussions on Solo-Q&Competitive w/ Cloud9 Wiggily

Order: Reorder
Duration: 3:41:56
Updated: 01 Dec 2017
views: 77673

videos

TIMESTAMPS BELOW This video is extremely long and covers an extremely wide range of topics all pertaining to the jungle. The Jungler who joins me in this video is NA Season 7's Rank 1 player and current Cloud9 professional Jungler Wiggily. 2:40 - Introduction 3:48 - CS Counting 5:15 - Rubberbanding 12:20 - Jungle/Lane Interactions in the West 14:15 - Rubberbanding Effects 15:08 - Rubberbanding Jungle Strategy 19:04 - Jungle Style Without Rubberbanding 20:51 - Season 8 Jungle Prediction -- Domination: 26:48 - Predator 29:35 - Electrocute 32:07 - Dark Harvest -- Precision: 34:29 - Press the Attack 37:26 - Lethal Tempo 38:30 - Fleet Footwork -- 41:15 - Precision Tree + Discussion 44:44 - Domination Tree + Discussion -- Resolve: 50:20 - Guardian 50:40 - Aftershock/Grasp 51:25 - Resolve Tree -- Inspiration: 54:20 - Keystones 55:12 - Inspiration Tree + Discussion -- Sorcery: 1:03:20 - Summon Aery 1:04:35 - Arcane Comet 1:05:42 - Phase Rush 1:06:12 - Socery Tree + Discussion -- 1:12:20 - Jungle Picks in NA 1:15:10 - Jungle Picks in KR 1:25:20 - Elise 1:28:52 - Ping -- Tier Lists: 1:32:00 - Soloq Tier List Start 2:39:12 - Soloq Tier List End 2:39:30 - Competitive Tier List Start 3:15:40 - Competitive Tier List End -- Twitter Questions: 3:16:00 - How to track the enemy jungler without wards now where the camps all count as 4? 3:24:35 - How to figure out your gank priority? 3:29:50 - Game knowledge that's associated with jungling in general 3:32:00 - Snowballing advantage -- 3:39:00 - Conclusion Follow Wiggily: https://twitter.com/lol_Wiggily Follow me: Twitter: http://twitter.com/LSXYZ9 Twitch: http://twitch.tv/imls Facebook: http://facebook.com/nick.decesare.7 Stream: http://twitch.tv/imls Instagram: http://instagram.com/LSXYZ9 Coaching Skype: Coaching is not available at this time Business E-mail: Nicholas.a.decesare at gmail dot com

https://wn.com/In_Depth_Jungle_Discussions_On_Solo_Q_Competitive_W_Cloud9_Wiggily

2:44:15

SynthEyes INTENSE - knowledge for Camera Tracking

Order: Reorder
Duration: 2:44:15
Updated: 08 Sep 2010
views: 70006

videos

This Tutorial was made during my free time. It is non-profit. In this intense 3D Tracking Tutorial I take you through syntheyes powerful Camera Tracking features. final result: http://www.youtube.com/watch?v=MeaFyFXRmGQ ********************************** WHAT YOU WILL LEARN: --1.--We will start by quickly discussing the principles behind camera tracking - Photogrammerty - to make you familar with matchmoving --2.--The following lessons will be spent on syntheyes User Interface and on Workflow optimization. --3.--Jumping right into Target Tracking we will discuss the matchmovers decision on wheather a feature is good to track or not, followed by an intense lesson on supervised Target Tracking with SynthEyes 2008 target functionalitys and the Graph Editor. --4.--We will automize the tracking and let SynthEyes find optional good features - and of course doing an auto-cleanup. --5.--Discussing fine-tuning, we will take a look at the variety of ways, syntheyes lets us optimize our camerapath smoothness and reduce pixel error. --6.--Of course we will also go into previsualizing the track quality and with some **Extra Lessons** we will discuss shot-stabilization and lens-distortion. **********************************END This tutorial was published for youtube use ONLY. For that reason I added an ANNOTATION SYSTEM that allowes you to quickly navigate the different chapters. Here is the INDEX to jump directly to different CHAPTERS from description: ************~~~~~~************* ########--INDEX-######## ______1. Introducing this tutorial & Tracking mathematics 00:00 - Introduction 03:18 - Photogrammerty made easy ______2. Introducing SynthEyes 12:25 - importing & basic footage settings 15:47 - image preperation 19:44 - SynthEyes Preferences 21:10 - coustomize the User Interface ______3. Tracking 25:00 - Auto Mode 29:12 - discovering features and trackers 38:35 - SynthEyes supervised tracking 58:14 - first solve & lens distortion optimization ______4. going advanced - auto tracker & constraining 1:09:37 - constraining and survery data 1:17:45 - solvehints & locks 1:19:18 - add automatic trackers ______5. going advanced - cleanup and optimization 1:29:39 - using & setting weights 1:34:27 - combine broken cams 1:38:18 - add many trackers 1:47:45 - auto-cleanup 1:58:05 - hold-regions 2:05:20 - spinal editing & path manipulation ______6. Output & Visualization 2:12:26 - image stabilization - basics 2:26:08 - image output 2:28:46 - perspective & testobjects 2:34:20 - prepare for export and remodellation ______ 2:41:40 - edning the tutorial ########--END-######## ***********~~~~~~************* Notice that in these tutorials I am working on a TUTORIAL BASE LEVEL, means while recording I WILL NOT GO FOR THE PERFECT RESULTS as I would when doing this for production. Don't judge my bad english, I am german and this is my first time recording a ~150 minutes LIVE tutorial.

https://wn.com/Syntheyes_Intense_Knowledge_For_Camera_Tracking

1:06:41

Introduction to Destruction with Simon Fenton - Escape Studios Webinar

Order: Reorder
Duration: 1:06:41
Updated: 08 Oct 2015
views: 796

videos

Simon took us through a destructive introduction to Unreal Engine 4 (UE4), demonstrating how to set up a real time fracture of an object and then, blow it up. UE4 is the same games engine that is used in the Gears of War and the Batman games - pretty cool, right? The explosive webinar looked at the physics systems in the graphics power-house that is Unreal Engine. Hit Subscribe for more! Follow us on Twitter! https://twitter.com/Escape_Studios Like us on Facebook! http://www.facebook.com/escapestudios

https://wn.com/Introduction_To_Destruction_With_Simon_Fenton_Escape_Studios_Webinar

24:59

Augmented Reality Tutorial No. 30: Fusion Effect using MultiTarget Tracking (Unity3D, Vuforia)

Order: Reorder
Duration: 24:59
Updated: 04 Aug 2015
views: 14275

videos

https://wn.com/Augmented_Reality_Tutorial_No._30_Fusion_Effect_Using_Multitarget_Tracking_(Unity3D,_Vuforia)

20:41

Pmomo: Projection Mapping on Movable 3D Object

Order: Reorder
Duration: 20:41
Updated: 31 May 2016
views: 388

videos

Pmomo: Projection Mapping on Movable 3D Object Yi Zhou, Shuangjiu Xiao, Ning Tang, Zhiyong Wei, Xu Chen CHI '16: ACM Conference on Human Factors in Computing Systems Session: Real Reality Interfaces Abstract We introduce Pmomo (acronym of projection mapping on movable object), a dynamic projection mapping system that tracks the 6-DOF position of real-world object, and shades it with virtual 3D contents by projection. The system can precisely lock the projection on the moving object in real-time, even the one with complex geometry. Based on depth camera, we developed a novel and robust tracking method that samples the structure of the object into low-density point cloud, then performs an adaptive searching scheme for the registration procedure. As a fully interactive system, our method can handle both internal and external complex occlusions, and can quickly track back the object even when losing track. In order to further improve the realism of the projected virtual textures, our system innovatively culls occlusions away from projection, which is achieved by a facet-covering method. As a result, the Pmomo system enables the possibility of new interactive Augmented Reality applications that require high-quality dynamic projection effect. DOI:: http://dx.doi.org/10.1145/2858036.2858329 WEB:: https://chi2016.acm.org/ Recorded at the 2016 CHI Conference on Human Factors in Computing Systems in San Jose, CA, United States, May 7-12, 2016

https://wn.com/Pmomo_Projection_Mapping_On_Movable_3D_Object

33:31

Visão Computacional com filtro de Kalman - Object Tracking

Order: Reorder
Duration: 33:31
Updated: 07 Jan 2016
views: 2536

videos

Explicação teórica dos principais conceitos envolvidos no projeto de rastreamento de objetos baseado em cor com filtro de Kalman desenvolvido na disciplina de Processamento Digital de Imagens da UTFPR Curitiba. Autor: Marcos Rogério Fernandes 2015.2 Demonstração do projeto em funcionamento: https://www.youtube.com/watch?v=tREbIw9DxHA

https://wn.com/Visão_Computacional_Com_Filtro_De_Kalman_Object_Tracking

42:23

Florian Wilhelm - Handling GPS Data with Python

Order: Reorder
Duration: 42:23
Updated: 06 Aug 2016
views: 4201

videos

Florian Wilhelm - Handling GPS Data with Python [EuroPython 2016] [22 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/handling-gps-data-with-python) If you have ever happened to need to deal with GPS data in Python you may have felt a bit lost. This talk presents libraries starting from basic reading and writing GPS tracks in the GPS Exchange Format to adding missing elevation information. Also visualisation of tracks on OpenStreetmap data with interactive plots in Jupyter notebooks is covered. Additionally common algorithms for GPS like Douglas-Peucker and Kalman filter are explained. ----- If you have ever happened to need to deal with GPS data in Python you may have felt a bit lost. There are many libraries at various states of maturity and scope. Finding a place to start and to actually work with the GPS data might not be as easy and obvious as you might expect from other Python domains. Inspired from my own experiences of dealing with GPS data in Python, I want to give an overview of some useful libraries. From basic reading and writing GPS tracks in the GPS Exchange Format with the help of gpxpy to adding missing elevation information with srtm.py. Additionally, I will cover mapping and visualising tracks on OpenStreetmap with mplleaflet that even supports interactive plots in a Jupyter notebook. Besides the tooling, I will also demonstrate and explain common algorithms like Douglas-Peucker to simplify a track and the famous Kalman filters for smoothing. For both algorithms I will give an intuition about how they work as well as their basic mathematical concepts. Especially the Kalman filter that is used for all kinds of sensor, not only GPS, has the reputation of being hard to understand. Still, its concept is really easy and quite comprehensible as I will also demonstrate by presenting an implementation in Python with the help of Numpy and Scipy. My presentation will make heavy use of the Jupyter notebook which is a wonderful tool perfectly suited for experimenting and learning.

https://wn.com/Florian_Wilhelm_Handling_Gps_Data_With_Python

1:00:48

Floating static route with IP SLA Tracking

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Duration: 1:00:48
Updated: 02 Apr 2012
views: 3340

videos

Floating static route with IP SLA Tracking فى الشبكات التى لها اكثر من مسار شرح استخدام Wan redundancy on Cisco device Use Static backup route with IP SLA Tracking How determine the state of primary connection without enable dynamic routing How ask the router to do some work on behalf you كيف نطلب من جهاز التوجيه للقيام ببعض الأعمال نيابة عنك http://www.facebook.com/elearning.NetwOrkers Hani Adel Cisco

https://wn.com/Floating_Static_Route_With_Ip_Sla_Tracking

1:25:55

Syfy UFO Documentary Government Secret Truth Exposed Aliens on the Moon Full HD 2015

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Duration: 1:25:55
Updated: 29 Dec 2014
views: 409820

videos

Unexplained and Unidentified Objects found one the Moon the Truth hast been Exposed about Aliens in 2015. All Government / NASA UFO secrets and mysteries explained. During the 1950s many U.F.O.S seen over Earth were tracked back to the Moon by government tracking stations in secret complexes in deserts in Arizona and Nevada and inside underground mountain bases. WE HAVE ONE PHOTO OF A SAUCER SHAPED CRAFT HOVERING OVER THE MOON, taken by a civilian astronomer. Sergeant Willard Wannail, who investigated U.F.O. landings on Ohio when in Army Intelligence, showed us an 8 by 10 CLEAR glossy detailed photo of a silvery spaceship hovering directly over a huge Moon landscape, estimated to be several miles long, and said to be CITY-SHIP designed to transport thousands of people between solar systems or galaxies and live for extended periods of time in self-sufficient orbiting communities!

https://wn.com/Syfy_Ufo_Documentary_Government_Secret_Truth_Exposed_Aliens_On_The_Moon_Full_Hd_2015

20:08

tutorial Unity and vuforia Augmented reality model 3d move forward

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Duration: 20:08
Updated: 02 Sep 2017
views: 728

videos

To show all videos and download all unity packages for free must be Subscriber youtube channel https://www.youtube.com/channel/UCNJVG9_IebHe-NF-K_Y8Grw?sub_confirmation=1 tutorial Unity : Augmented reality ar vuforia * move forward 3d model by button * how used Extended Tracking to moving

https://wn.com/Tutorial_Unity_And_Vuforia_Augmented_Reality_Model_3D_Move_Forward