• 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
Circular extended object tracking with the Particle Filter
1:33

Circular extended object tracking with the Particle Filter

  • Order:
  • 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
Circular extended object tracking with the box particle filter
3:06

Circular extended object tracking with the box particle filter

  • Order:
  • 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
Augmented Reality Vuforia Extended Tracking Keep Object Even The Target Lost
4:15

Augmented Reality Vuforia Extended Tracking Keep Object Even The Target Lost

  • Order:
  • 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
Object Tracking with Sensor Fusion-based Extended Kalman Filter
0:48

Object Tracking with Sensor Fusion-based Extended Kalman Filter

  • Order:
  • 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
Directional Moving Object Tracking in 2D with the Extended Kalman Filter on Matrix Lie Groups
2:37

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

  • Order:
  • 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
Sensor Fusion for Object Tracking
24:56

Sensor Fusion for Object Tracking

  • Order:
  • 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
Extended Kalman Filter for object tracking
0:36

Extended Kalman Filter for object tracking

  • Order:
  • 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
Object tracking using an extended kalman filter (EKF) with Lidar and Radar measurements
0:12

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

  • Order:
  • 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
[Extended Demo] Robust 3D Object Trackinf fro Monocular Images using Stable Parts
1:00

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

  • Order:
  • 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
Real-time tracking occluded object using CamShift and Kalman Filter algorithms.
0:21

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

  • Order:
  • 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.
3D Object Tracking for Augmented Reality | Srushti Labs
1:07

3D Object Tracking for Augmented Reality | Srushti Labs

  • Order:
  • 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
Multiple objects tracking in the presence of long term occlusions
2:39

Multiple objects tracking in the presence of long term occlusions

  • Order:
  • 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
Model Targets  - Vuforia's latest object recognition technology
0:29

Model Targets - Vuforia's latest object recognition technology

  • Order:
  • 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
Motion-based Object Detection and Tracking Using 3D-LIDAR
0:23

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

  • Order:
  • 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
Laser Radar Object Tracking
0:16

Laser Radar Object Tracking

  • Order:
  • Duration: 0:16
  • Updated: 24 Dec 2017
  • views: 10
videos https://wn.com/Laser_Radar_Object_Tracking
Object tracking with Sensor Fusion-based Extended Kalman Filter
0:20

Object tracking with Sensor Fusion-based Extended Kalman Filter

  • Order:
  • 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
object tracking using Kalman filter
10:26

object tracking using Kalman filter

  • Order:
  • 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
Visual Object Tracking using Powell's Direct Set Method and Kalman Filtering
2:47

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

  • Order:
  • 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
Kudan : Extended Tracking and Detection Feature
1:29

Kudan : Extended Tracking and Detection Feature

  • Order:
  • Duration: 1:29
  • Updated: 25 Nov 2016
  • views: 4198
videos
https://wn.com/Kudan_Extended_Tracking_And_Detection_Feature
Vuforia 3d Object Tracking Test
0:29

Vuforia 3d Object Tracking Test

  • Order:
  • 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
Object tracking with 2D Kalman Filter part 1: Matlab implimentation by Student Dave
11:49

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

  • Order:
  • 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
Combining Shape-Changing Interfaces and Spatial Augmented Reality Enables Extended Object Appearance
0:31

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

  • Order:
  • 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
Synthetic Aperture Tracking: Tracking through Occlusions
4:46

Synthetic Aperture Tracking: Tracking through Occlusions

  • Order:
  • 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
vuforia 3d object tracking
1:26

vuforia 3d object tracking

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  • Duration: 1:26
  • Updated: 03 May 2017
  • views: 2971
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https://wn.com/Vuforia_3D_Object_Tracking
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