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
Object Detection & Tracking from UAV

Detection(white box) - Variance Filter - HOG and Random Ferns Feature - Adaboost Cascade Classifier Tracking(red box) - Extended Kalman Filter eyedea inc. eyedea@eyedea.co.kr

published: 02 Feb 2016
kalman filter object tracking vot2015 basketball sequence

Constant velocity model is used as motion model. n=4 and m=2. Observation noise variance is 25 (std=5 and this is used to add normal noise to ground truth locations). Process covariance matrix, about the diagonal elements: variance for location is 0.1 and variance for velocity is 0.01.

published: 07 Jun 2016
Long-term Robust Visual Tracking via Temporal Learning and Deep Neural Networks

Long-term Robust Visual Tracking via Temporal Learning and Deep Neural Networks by Shu Wang from Rutgers University. Methods for comparison: MOSSE: D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui. Visual Object Tracking using Adaptive Correlation Filters. CVPR, 2010. https://sites.google.com/site/dbolme/visual-tracking KCF: J. F. Henriques, R. Caseiro, P. Martins, J. Batista High-Speed Tracking with Kernelized Correlation Filters TPAMI, 2015 http://home.isr.uc.pt/~henriques/circulant/ ACT: Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg and Joost van de Weijer. Adaptive Color Attributes for Real-Time Visual Tracking. CVPR, 2014. http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html DSST: Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan an...

published: 18 Nov 2015
Object Tracking #1

published: 03 Mar 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
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
Multiple object tracking with kalman tracker and sort

An experiment on Oxford Town Centre Dataset. More details here: https://github.com/ZidanMusk/experimenting-with-sort

published: 24 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
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
Laser Radar Object Tracking

Udacity Extended Kalman Filter

published: 24 Dec 2017
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
vuforia 3d object tracking

published: 03 May 2017
Vuforia Object tracking

published: 18 Jan 2017
HOW TO AUGMENTED REALITY TUTORIAL ¦ WIKITUDE OBJECT TRACKING

If you would like to download the new Wikitude SDK7 click the link: http://bit.ly/WikitudeDownloadSDK . All views expressed are my own. Please consider supporting Realistic Effect creator: https://www.assetstore.unity3d.com/#!/content/85675?aid=1101lITW Tutorial assets provided here: https://www.ourtechart.com/augmented-reality/ar-wikitude-object-tracking/

published: 13 Jul 2017
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
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
Rigid Object Tracking for Augmented Reality

Object tracking with Iterative Closest Points algorithm and a range camera (Kinect in this case). The video show the raw tracking outcome without any filters, etc.

published: 26 Nov 2014
Multi Object Tracking Tutorial: part 3 by Student Dave

This is the first part of the image processing in MATLAB for the object tracking. Application of blob filter! Visit website for code http://studentdavestutorials.weebly.com/

published: 30 Jan 2013

developed with YouTube

1:33

Circular extended object tracking with the Particle Filter

Order: Reorder
Duration: 1:33
Updated: 20 Feb 2015
views: 173

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: 2499

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: 987

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: 163

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

8:52

Object Detection & Tracking from UAV

Order: Reorder
Duration: 8:52
Updated: 02 Feb 2016
views: 437

videos

Detection(white box) - Variance Filter - HOG and Random Ferns Feature - Adaboost Cascade Classifier Tracking(red box) - Extended Kalman Filter eyedea inc. eyedea@eyedea.co.kr

https://wn.com/Object_Detection_Tracking_From_Uav

0:25

kalman filter object tracking vot2015 basketball sequence

Order: Reorder
Duration: 0:25
Updated: 07 Jun 2016
views: 619

videos

Constant velocity model is used as motion model. n=4 and m=2. Observation noise variance is 25 (std=5 and this is used to add normal noise to ground truth locations). Process covariance matrix, about the diagonal elements: variance for location is 0.1 and variance for velocity is 0.01.

https://wn.com/Kalman_Filter_Object_Tracking_Vot2015_Basketball_Sequence

6:11

Long-term Robust Visual Tracking via Temporal Learning and Deep Neural Networks

Order: Reorder
Duration: 6:11
Updated: 18 Nov 2015
views: 15371

videos

Long-term Robust Visual Tracking via Temporal Learning and Deep Neural Networks by Shu Wang from Rutgers University. Methods for comparison: MOSSE: D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui. Visual Object Tracking using Adaptive Correlation Filters. CVPR, 2010. https://sites.google.com/site/dbolme/visual-tracking KCF: J. F. Henriques, R. Caseiro, P. Martins, J. Batista High-Speed Tracking with Kernelized Correlation Filters TPAMI, 2015 http://home.isr.uc.pt/~henriques/circulant/ ACT: Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg and Joost van de Weijer. Adaptive Color Attributes for Real-Time Visual Tracking. CVPR, 2014. http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html DSST: Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan and Michael Felsberg. Accurate Scale Estimation for Robust Visual Tracking. BMVC, 2014. http://www.cvl.isy.liu.se/research/objrec/visualtracking/scalvistrack/index.html TLD: Z. Kalal, K. Mikolajczyk, and J. Matas, Tracking-Learning-Detection. TPAMI, 2011. http://personal.ee.surrey.ac.uk/Personal/Z.Kalal/index.html

https://wn.com/Long_Term_Robust_Visual_Tracking_Via_Temporal_Learning_And_Deep_Neural_Networks

4:27

Object Tracking #1

Order: Reorder
Duration: 4:27
Updated: 03 Mar 2017
views: 593

videos

https://wn.com/Object_Tracking_1

0:20

Object tracking with Sensor Fusion-based Extended Kalman Filter

Order: Reorder
Duration: 0:20
Updated: 03 May 2017
views: 262

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

0:29

Vuforia 3d Object Tracking Test

Order: Reorder
Duration: 0:29
Updated: 28 Aug 2016
views: 2222

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

3:01

Multiple object tracking with kalman tracker and sort

Order: Reorder
Duration: 3:01
Updated: 24 Jul 2017
views: 6374

videos

An experiment on Oxford Town Centre Dataset. More details here: https://github.com/ZidanMusk/experimenting-with-sort

https://wn.com/Multiple_Object_Tracking_With_Kalman_Tracker_And_Sort

1:00

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

Order: Reorder
Duration: 1:00
Updated: 30 Jun 2017
views: 363

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

0:36

Extended Kalman Filter for object tracking

Order: Reorder
Duration: 0:36
Updated: 24 May 2017
views: 55

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: 53

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

0:16

Laser Radar Object Tracking

Order: Reorder
Duration: 0:16
Updated: 24 Dec 2017
views: 11

videos

Udacity Extended Kalman Filter

https://wn.com/Laser_Radar_Object_Tracking

2:39

Multiple objects tracking in the presence of long term occlusions

Order: Reorder
Duration: 2:39
Updated: 25 Nov 2010
views: 26031

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

1:26

vuforia 3d object tracking

Order: Reorder
Duration: 1:26
Updated: 03 May 2017
views: 3075

videos

https://wn.com/Vuforia_3D_Object_Tracking

0:14

Vuforia Object tracking

Order: Reorder
Duration: 0:14
Updated: 18 Jan 2017
views: 423

videos

https://wn.com/Vuforia_Object_Tracking

12:33

HOW TO AUGMENTED REALITY TUTORIAL ¦ WIKITUDE OBJECT TRACKING

Order: Reorder
Duration: 12:33
Updated: 13 Jul 2017
views: 6608

videos

If you would like to download the new Wikitude SDK7 click the link: http://bit.ly/WikitudeDownloadSDK . All views expressed are my own. Please consider supporting Realistic Effect creator: https://www.assetstore.unity3d.com/#!/content/85675?aid=1101lITW Tutorial assets provided here: https://www.ourtechart.com/augmented-reality/ar-wikitude-object-tracking/

https://wn.com/How_To_Augmented_Reality_Tutorial_¦_Wikitude_Object_Tracking

2:47

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

Order: Reorder
Duration: 2:47
Updated: 23 Jan 2013
views: 7932

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

0:23

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

Order: Reorder
Duration: 0:23
Updated: 29 May 2016
views: 1483

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

2:07

Rigid Object Tracking for Augmented Reality

Order: Reorder
Duration: 2:07
Updated: 26 Nov 2014
views: 927

videos

Object tracking with Iterative Closest Points algorithm and a range camera (Kinect in this case). The video show the raw tracking outcome without any filters, etc.

https://wn.com/Rigid_Object_Tracking_For_Augmented_Reality

7:47

Multi Object Tracking Tutorial: part 3 by Student Dave

Order: Reorder
Duration: 7:47
Updated: 30 Jan 2013
views: 10335

videos

This is the first part of the image processing in MATLAB for the object tracking. Application of blob filter! Visit website for code http://studentdavestutorials.weebly.com/

https://wn.com/Multi_Object_Tracking_Tutorial_Part_3_By_Student_Dave

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
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
Hướng dẫn 3D Object Tracking trong Affter Effect CS6 - Phần 1

Đây là phần 1 của series Tuts hướng dẫn sử dụng chức năng 3D Tracking trong After Effect CS6.

published: 21 Dec 2012
Control Bootcamp: Kalman Filter Example in Matlab

This lecture explores the Kalman Filter in Matlab on the example of an inverted pendulum on a cart. Code available at: faculty.washington.edu/sbrunton/control_bootcamp_code.zip These lectures follow Chapters 1 & 3 from: Machine learning control, by Duriez, Brunton, & Noack https://www.amazon.com/Machine-Learning-Control-Turbulence-Applications-ebook/dp/B01MDUPONF/ Chapters available at: http://faculty.washington.edu/sbrunton/mlcbook/

published: 07 Feb 2017
Blender Beginners Tutorial: Create A 3d Pawn Chess Piece Or Cylindrical Object From An Image Using G

In this video I will show you how to create a solid 3d cylindrical object from an image by first creating an svg image in Gimp image editor, and than converting the svg image to a solid 3d object in Blender. Blender is one of the best free software/freeware editing packages available for Windows, Linux & Apple. Link to image https://plus.google.com/b/105695297949434078487/105695297949434078487/posts/RqXz4mFEHCm?pageId=105695297949434078487&pid=6291617329936626418&oid=105695297949434078487 Blender is a professional, free and open-source 3D computer graphics software toolset used for creating animated films, visual effects, art, 3D printed models, interactive 3D applications and video games. Blender's features include 3D modeling, UV unwrapping, texturing, raster graphics editing, riggin...

published: 04 Jun 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
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
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
People Detection and Tracking in RGB-D Data by Matteo Munaro @ PCL Hackfest June 2016

PCL Hackfest June 2016 The Open Session presentation of "People Detection and Tracking in RGB-D Data" by Matteo Munaro from University of Padua. Slides: http://pointclouds.org/hackfest/assets/slides/matteo.pdf Video: https://www.youtube.com/watch?v=gUbobrBpYVc Playlist: https://www.youtube.com/playlist?list=PLawGHFDTLKvIXdrIoAkwn4ZOg66PSK__3

published: 01 Jul 2016
28c3: KinectFusion

Download high quality version: http://bit.ly/sLlP1s Description: http://events.ccc.de/congress/2011/Fahrplan/events/4928.en.html David Kim: KinectFusion Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera This project investigates techniques to track the 6DOF position of handheld depth sensing cameras, such as Kinect, as they move through space and perform high quality 3D surface reconstructions for interaction. While depth cameras are not conceptually new, Kinect has made such sensors accessible to all. The quality of the depth sensing, given the low-cost and real-time nature of the device, is compelling, and has made the sensor instantly popular with researchers and enthusiasts alike. The Kinect camera uses a structured light technique to generate real-time...

published: 30 Dec 2011
Kalman Filter Tutorial in MATLAB

published: 13 Apr 2015
Fuel your passion: Flame Premium 2014 new creative capabilities

http://www.autodesk.com/flamepremium Fuel your passion for visual effects creation with Flame Premium 2014. Learn how the new 3D warping and morphing capabilities, extended bicubic tracking enhancements, and performance driven story-centric effects help you be more creative, work faster, and push the limits.

published: 25 Sep 2013
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
NIBIRU HOAX _ SHAPE SHIFTING OBJECT IN SPACE ~ HARVEST FULL MOON MAUI 9_12 _11.flv

NIBIRU HOAX ? SHAPE SHIFTING OBJECT IN SPACE ~ HARVEST FULL MOON MAUI 9/12 /11 From: BrutalTruthMediaMaui | Sep 13, 2011 | 2,057 views Loading... SPACED BASED LASERS ARE NO JOKE,THEY ARE WEAPONS OF MASS DESTRUCTION,NASA DENIES IT OF COURSE.They work with the HAARP & Project Blue Beam, the chem clouds/plasma are the transmitters ,they can be used as mirrors also.. Read below,it will show you how involved Hawaii is in the HAARP'S global tectonic warfare agenda starting way back in 1985 ! and i am sure it was earlier then that even. .. Where is all the government's money going,well,here is one of the main star wars weaponry agenda's being sponsored & financed by our government ....can you only imagine how many TRILLION'S are being spent now if this account was from 1985 ! Bet...

published: 18 Sep 2011
The Kalman Filter. 54.1833607, -1.9391344

Music and Imagery

published: 23 Apr 2017
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
The Kalman Filter

Speaker: Justin Pearson (Electrical and Computer Engineering) Title: The Kalman Filter: An unreasonably good state estimator Date: Nov 24, 2014 Location: 1601 Elings Hall, University of California, Santa Barbara The Kalman Filter stirred up excitement in the aerospace industry in the 60's as an intuitive, optimal, and easily-implementable state estimator. It allowed control engineers to employ celebrated state-feedback controllers even when the system's state was not directly measurable, in a plug-and-play sort of way. This excitement spread to other industries, who discovered that the filter works unreasonably well even when violating the assumptions underlying the design of the filter, like having a non-Gaussian noise distribution. Several extensions of the KF apply the same bas...

published: 19 Oct 2016
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
code::dive conference 2015 - Dan Saks - Representing Memory Mapped Devices as Objects

code::dive conference 2015 - Nokia Wrocław http://codedive.pl/

published: 14 Jan 2016

developed with YouTube

24:56

Sensor Fusion for Object Tracking

Order: Reorder
Duration: 24:56
Updated: 02 Sep 2017
views: 6878

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

33:28

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

Order: Reorder
Duration: 33:28
Updated: 06 Oct 2016
views: 367

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

1:00:04

Hướng dẫn 3D Object Tracking trong Affter Effect CS6 - Phần 1

Order: Reorder
Duration: 1:00:04
Updated: 21 Dec 2012
views: 2778

videos

Đây là phần 1 của series Tuts hướng dẫn sử dụng chức năng 3D Tracking trong After Effect CS6.

https://wn.com/Hướng_Dẫn_3D_Object_Tracking_Trong_Affter_Effect_Cs6_Phần_1

22:12

Control Bootcamp: Kalman Filter Example in Matlab

Order: Reorder
Duration: 22:12
Updated: 07 Feb 2017
views: 22597

videos

This lecture explores the Kalman Filter in Matlab on the example of an inverted pendulum on a cart. Code available at: faculty.washington.edu/sbrunton/control_bootcamp_code.zip These lectures follow Chapters 1 & 3 from: Machine learning control, by Duriez, Brunton, & Noack https://www.amazon.com/Machine-Learning-Control-Turbulence-Applications-ebook/dp/B01MDUPONF/ Chapters available at: http://faculty.washington.edu/sbrunton/mlcbook/

https://wn.com/Control_Bootcamp_Kalman_Filter_Example_In_Matlab

23:52

Blender Beginners Tutorial: Create A 3d Pawn Chess Piece Or Cylindrical Object From An Image Using G

Order: Reorder
Duration: 23:52
Updated: 04 Jun 2016
views: 1281

videos

In this video I will show you how to create a solid 3d cylindrical object from an image by first creating an svg image in Gimp image editor, and than converting the svg image to a solid 3d object in Blender. Blender is one of the best free software/freeware editing packages available for Windows, Linux & Apple. Link to image https://plus.google.com/b/105695297949434078487/105695297949434078487/posts/RqXz4mFEHCm?pageId=105695297949434078487&pid=6291617329936626418&oid=105695297949434078487 Blender is a professional, free and open-source 3D computer graphics software toolset used for creating animated films, visual effects, art, 3D printed models, interactive 3D applications and video games. Blender's features include 3D modeling, UV unwrapping, texturing, raster graphics editing, rigging and skinning, fluid and smoke simulation, particle simulation, soft body simulation, sculpting, animating, match moving, camera tracking, rendering, motion graphics, video editing and compositing. It also features an integrated game engine.

https://wn.com/Blender_Beginners_Tutorial_Create_A_3D_Pawn_Chess_Piece_Or_Cylindrical_Object_From_An_Image_Using_G

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: 1689

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: 2773

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

24:04

Astrophotography without a star tracker

Order: Reorder
Duration: 24:04
Updated: 24 Feb 2012
views: 309345

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

33:31

Visão Computacional com filtro de Kalman - Object Tracking

Order: Reorder
Duration: 33:31
Updated: 07 Jan 2016
views: 2617

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

24:44

People Detection and Tracking in RGB-D Data by Matteo Munaro @ PCL Hackfest June 2016

Order: Reorder
Duration: 24:44
Updated: 01 Jul 2016
views: 708

videos

PCL Hackfest June 2016 The Open Session presentation of "People Detection and Tracking in RGB-D Data" by Matteo Munaro from University of Padua. Slides: http://pointclouds.org/hackfest/assets/slides/matteo.pdf Video: https://www.youtube.com/watch?v=gUbobrBpYVc Playlist: https://www.youtube.com/playlist?list=PLawGHFDTLKvIXdrIoAkwn4ZOg66PSK__3

https://wn.com/People_Detection_And_Tracking_In_Rgb_D_Data_By_Matteo_Munaro_Pcl_Hackfest_June_2016

1:06:31

28c3: KinectFusion

Order: Reorder
Duration: 1:06:31
Updated: 30 Dec 2011
views: 4388

videos

Download high quality version: http://bit.ly/sLlP1s Description: http://events.ccc.de/congress/2011/Fahrplan/events/4928.en.html David Kim: KinectFusion Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera This project investigates techniques to track the 6DOF position of handheld depth sensing cameras, such as Kinect, as they move through space and perform high quality 3D surface reconstructions for interaction. While depth cameras are not conceptually new, Kinect has made such sensors accessible to all. The quality of the depth sensing, given the low-cost and real-time nature of the device, is compelling, and has made the sensor instantly popular with researchers and enthusiasts alike. The Kinect camera uses a structured light technique to generate real-time depth maps containing discrete range measurements of the physical scene. This data can be reprojected as a set of discrete 3D points (or point cloud). Even though the Kinect depth data is compelling, particularly compared to other commercially available depth cameras, it is still inherently noisy. Depth mea- surements often fluctuate and depth maps contain numerous 'holes' where no readings were obtained. To generate 3D models for use in applications such as gaming, physics, or CAD, higher-level surface geometry needs to be inferred from this noisy point-based data. One simple approach makes strong assumptions about the connectivity of neighboring points within the Kinect depth map to generate a mesh representation. This, however, leads to noisy and low-quality meshes. As importantly, this approach creates an incomplete mesh, from only a single, fixed viewpoint. To create a complete (or even watertight) 3D model, different viewpoints of the physical scene must be captured and fused into a single representation. This talk presents a novel interactive reconstruction system called KinectFusion). The system takes live depth data from a moving Kinect camera and, in real- time, creates a single high-quality, geometrically accurate, 3D model. A user holding a standard Kinect camera can move within any indoor space, and reconstruct a 3D model of the physical scene within seconds. The system continuously tracks the 6 degrees-of-freedom (DOF) pose of the camera and fuses new viewpoints of the scene into a global surface-based representation. A novel GPU pipeline allows for accurate camera tracking and surface reconstruction at interactive real-time rates. We demonstrate core uses of KinectFusion as a low-cost handheld scanner, and present novel interactive methods for segmenting physical objects of interest from the reconstructed scene. We show how a real-time 3D model can be leveraged for geometry-aware augmented reality (AR) and physics- based interactions, where virtual worlds more realistically merge and interact with the real. Placing such systems into an interaction context, where users need to dynamically interact in front of the sensor, reveals a fundamental challenge -- no longer can we assume a static scene for camera tracking or reconstruction. We illustrate failure cases caused by a user moving in front of the sensor. We describe new meth ods to overcome these limitations, allowing camera tracking and reconstruction of a static background scene, while simultaneously segmenting, reconstructing and tracking foreground objects, including the user. We use this approach to demonstrate real-time multi-touch inter actions anywhere, allowing a user to appropriate any physical surface, be it planar or non-planar, for touch.

https://wn.com/28C3_Kinectfusion

44:35

Kalman Filter Tutorial in MATLAB

Order: Reorder
Duration: 44:35
Updated: 13 Apr 2015
views: 28737

videos

https://wn.com/Kalman_Filter_Tutorial_In_Matlab

44:47

Fuel your passion: Flame Premium 2014 new creative capabilities

Order: Reorder
Duration: 44:47
Updated: 25 Sep 2013
views: 3451

videos

http://www.autodesk.com/flamepremium Fuel your passion for visual effects creation with Flame Premium 2014. Learn how the new 3D warping and morphing capabilities, extended bicubic tracking enhancements, and performance driven story-centric effects help you be more creative, work faster, and push the limits.

https://wn.com/Fuel_Your_Passion_Flame_Premium_2014_New_Creative_Capabilities

42:23

Florian Wilhelm - Handling GPS Data with Python

Order: Reorder
Duration: 42:23
Updated: 06 Aug 2016
views: 4361

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

21:56

NIBIRU HOAX _ SHAPE SHIFTING OBJECT IN SPACE ~ HARVEST FULL MOON MAUI 9_12 _11.flv

Order: Reorder
Duration: 21:56
Updated: 18 Sep 2011
views: 1529

videos

NIBIRU HOAX ? SHAPE SHIFTING OBJECT IN SPACE ~ HARVEST FULL MOON MAUI 9/12 /11 From: BrutalTruthMediaMaui | Sep 13, 2011 | 2,057 views Loading... SPACED BASED LASERS ARE NO JOKE,THEY ARE WEAPONS OF MASS DESTRUCTION,NASA DENIES IT OF COURSE.They work with the HAARP & Project Blue Beam, the chem clouds/plasma are the transmitters ,they can be used as mirrors also.. Read below,it will show you how involved Hawaii is in the HAARP'S global tectonic warfare agenda starting way back in 1985 ! and i am sure it was earlier then that even. .. Where is all the government's money going,well,here is one of the main star wars weaponry agenda's being sponsored & financed by our government ....can you only imagine how many TRILLION'S are being spent now if this account was from 1985 ! Between fiscal years 1985 and 1991, SD10 received from our government $ billion's for research and development in technology (weather wars) areas. About $9.3 billion was for spaced based sensors, $11.9 billion for directed energy weapons (DEW), $7.8 billion for kinetic energy weapons (KEW), $2.7 billion for systems analysis and battle management (SA & BM), and $4.2 billion for survivability, lethality, and other key technologies (SLKT). The Department of Energy received $5.6 billion for research on space-based nuclear power sources for SD1 weapons, X-ray laser research, and other SD1 research. ....and then we wonder why there are so many starving children in the world,because war is more important then saving humanity..this is indeed the dark age of kali yuga. The High Precision Tracking Experiment (HPTE), launched with the Space Shuttle Discovery on STS-51-G, was tested June 21, 1985 when a Hawaii-based low-power laser successfully tracked the experiment and bounced the laser off of the HPTE mirror. The Relay mirror experiment (RME), launched in February 1990, demonstrated critical technologies for space-based relay mirrors that would be used with an SDI directed-energy weapon system. The experiment validated stabilization, tracking, and pointing concepts and proved that a laser could be relayed from the ground to a 60 cm mirror on an orbiting satellite and back to another ground station with a high degree of accuracy and for extended durations. I was somewhat limited because of my cam corder but i did zoom in as far as i could while filming..The object itself was zooming in closer to earth,then zooming back out again,or it was just shrinking in size,im not quite sure. ( im sure good old nwo order funded & trustworthy (SIKE) NASA knows what it REALLY is ) It think it's involved with star wars weaponry / project blue beam. ..The background sound are the crashing waves upon the sea shore.. I walked down to the beach right near where i live to film this peculiar unidentified (in my mind) moving object in space..This supposed Nibiru Dwarf Star kept morphing in and out of all sorts of shapes and shades,then it would glow very brightly,and then it would start to dim way down,its blatantly obvious to me this may not be a dwarf star at all,BUT then who really know anything anymore,this is simply my opinion..I think its MOST LIKELY some sort of possible star wars weaponry space craft etc being pushed down our throats as a natural phenomenon,yeah right ! the gullible masses are sucking up this hoax like a crack addict ..The camera was set on a tripod most of my filming,so the movement was not me but only in a few very short spots, the object was bouncing all around in the sky.. i only moved the camera a few times in this vlog,but then i purposely will let you know when i do so that you know its me and not this object moving..i want you to understand that i do not know what to think of this,hoax or no hoax ? ,im leaning towards hoax,because nasa is sheer deception ..but i will also try to respect everyone else 's opinion and views even if i do not agree,because ,i know there will be a lot of different opinions and explanations and views from different minds on the topic ,probably a few people are going to totally disagree with me and call me delusional and how can i doubt NASA's credibility,but im really cool with people who want to disagree ,but this whole nibiru story does not ring true to me even if the whole duped world says its true and thinks i'm loony. ..

https://wn.com/Nibiru_Hoax_Shape_Shifting_Object_In_Space_~_Harvest_Full_Moon_Maui_9_12_11.Flv

30:05

The Kalman Filter. 54.1833607, -1.9391344

Order: Reorder
Duration: 30:05
Updated: 23 Apr 2017
views: 312

videos

Music and Imagery

https://wn.com/The_Kalman_Filter._54.1833607,_1.9391344

20:08

tutorial Unity and vuforia Augmented reality model 3d move forward

Order: Reorder
Duration: 20:08
Updated: 02 Sep 2017
views: 778

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

47:16

The Kalman Filter

Order: Reorder
Duration: 47:16
Updated: 19 Oct 2016
views: 5029

videos

Speaker: Justin Pearson (Electrical and Computer Engineering) Title: The Kalman Filter: An unreasonably good state estimator Date: Nov 24, 2014 Location: 1601 Elings Hall, University of California, Santa Barbara The Kalman Filter stirred up excitement in the aerospace industry in the 60's as an intuitive, optimal, and easily-implementable state estimator. It allowed control engineers to employ celebrated state-feedback controllers even when the system's state was not directly measurable, in a plug-and-play sort of way. This excitement spread to other industries, who discovered that the filter works unreasonably well even when violating the assumptions underlying the design of the filter, like having a non-Gaussian noise distribution. Several extensions of the KF apply the same basic concepts to nonlinear systems (the extended / unscented Kalman Filter) and statistically-simulated systems (the particle filter). In this talk, I will explain the basics of control theory and linear systems, describe the Kalman Filter, and show some cool examples. ... Sorry about the occasionally fuzzy audio!

https://wn.com/The_Kalman_Filter

24:59

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

Order: Reorder
Duration: 24:59
Updated: 04 Aug 2015
views: 14635

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-multitarget-tracking/

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