• Multi Object Tracking Tutorial: part 1 by Student Dave

    Very simple example of Multi object tracking using the Kalman filter and then Hungarian algorithm. Visit website for code http://studentdavestutorials.weebly.com/ if you would like get those lil bugs, http://www.hexbug.com/nano/

    published: 30 Jan 2013
  • 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
  • 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
  • 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
  • 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
  • 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
  • Object tracking with 2D Kalman Filter part 2: Matlab implimentation by Student Dave

    This code implements a 2-d tracking of object in an image with 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/ this tutorial features MATLAB® programming language, go here of you wanna get it :) http://www.mathworks.com/products/matlab/

    published: 19 Dec 2012
  • 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
  • AR: 3D Object Tracking

    Object-Based mobile AR for 3D untextured building blocks using Vuforia and jPCT-AE.

    published: 08 Apr 2017
  • Kalman Filter Object Tracking

    A high-speed camera was used with a Kalman filter to track the position, velocity, and acceleration of an object in free fall. For more information please visit: http://www.acasper.org/2012/03/07/kalman-filtering/

    published: 06 Mar 2012
  • Object-Tracking AR

    published: 15 Jun 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
  • ELG3336 "Following Flame" Object Tracking Robot

    ELG3336 Object Tracking Robot Project by Erika and Darik -goal was to create a low-budget interpretation of a person following robot that could be easily extended to a more useful product such as a luggage carrying robot

    published: 11 Jan 2013
  • 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
  • vuforia 3d object tracking

    published: 03 May 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 Object tracking

    published: 18 Jan 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. The C++ code can be found here "https://github.com/Heych88/udacity-sdcnd-extended-kalman-filter"

    published: 30 Jul 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 with Kalman filter (Java Programming language)

    published: 05 Jul 2013
  • 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
  • 77 GHz Radar, Multiple object tracking, two people passing

    High-end 77-GHz 4D radar system, Wide field-of-view, Unique algorithms to detect problematic slow-moving and stationary targets, Optional camera installation for reference and sensor fusion opportunities. Suitable for Autonomous Drive applications.

    published: 05 Jul 2016
developed with YouTube
Multi Object Tracking Tutorial: part 1  by Student Dave

Multi Object Tracking Tutorial: part 1 by Student Dave

  • Order:
  • Duration: 9:46
  • Updated: 30 Jan 2013
  • views: 17354
videos
Very simple example of Multi object tracking using the Kalman filter and then Hungarian algorithm. Visit website for code http://studentdavestutorials.weebly.com/ if you would like get those lil bugs, http://www.hexbug.com/nano/
https://wn.com/Multi_Object_Tracking_Tutorial_Part_1_By_Student_Dave
Circular extended object tracking with the Particle Filter

Circular extended object tracking with the Particle Filter

  • Order:
  • Duration: 1:33
  • Updated: 20 Feb 2015
  • views: 157
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

Circular extended object tracking with the box particle filter

  • Order:
  • Duration: 3:06
  • Updated: 04 Feb 2015
  • views: 107
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
Object Tracking with Sensor Fusion-based Extended Kalman Filter

Object Tracking with Sensor Fusion-based Extended Kalman Filter

  • Order:
  • Duration: 0:48
  • Updated: 02 May 2017
  • views: 510
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
Multiple objects tracking in the presence of long term occlusions

Multiple objects tracking in the presence of long term occlusions

  • Order:
  • Duration: 2:39
  • Updated: 25 Nov 2010
  • views: 24718
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
Object tracking with 2D Kalman Filter part 1: Matlab implimentation by Student Dave

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

  • Order:
  • Duration: 11:49
  • Updated: 19 Dec 2012
  • views: 43502
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
Model Targets  - Vuforia's latest object recognition technology

Model Targets - Vuforia's latest object recognition technology

  • Order:
  • Duration: 0:29
  • Updated: 26 Jun 2017
  • views: 12783
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

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

  • Order:
  • Duration: 0:23
  • Updated: 29 May 2016
  • views: 814
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
Object tracking with 2D Kalman Filter part 2: Matlab implimentation by Student Dave

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

  • Order:
  • Duration: 7:44
  • Updated: 19 Dec 2012
  • views: 30878
videos
This code implements a 2-d tracking of object in an image with 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/ this tutorial features MATLAB® programming language, go here of you wanna get it :) http://www.mathworks.com/products/matlab/
https://wn.com/Object_Tracking_With_2D_Kalman_Filter_Part_2_Matlab_Implimentation_By_Student_Dave
Vuforia 3d Object Tracking Test

Vuforia 3d Object Tracking Test

  • Order:
  • Duration: 0:29
  • Updated: 28 Aug 2016
  • views: 1717
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
AR: 3D Object Tracking

AR: 3D Object Tracking

  • Order:
  • Duration: 1:27
  • Updated: 08 Apr 2017
  • views: 191
videos
Object-Based mobile AR for 3D untextured building blocks using Vuforia and jPCT-AE.
https://wn.com/Ar_3D_Object_Tracking
Kalman Filter Object Tracking

Kalman Filter Object Tracking

  • Order:
  • Duration: 0:27
  • Updated: 06 Mar 2012
  • views: 888
videos
A high-speed camera was used with a Kalman filter to track the position, velocity, and acceleration of an object in free fall. For more information please visit: http://www.acasper.org/2012/03/07/kalman-filtering/
https://wn.com/Kalman_Filter_Object_Tracking
Object-Tracking AR

Object-Tracking AR

  • Order:
  • Duration: 0:33
  • Updated: 15 Jun 2016
  • views: 127
videos
https://wn.com/Object_Tracking_Ar
Sensor Fusion for Object Tracking

Sensor Fusion for Object Tracking

  • Order:
  • Duration: 24:56
  • Updated: 02 Sep 2017
  • views: 2999
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
ELG3336 "Following Flame" Object Tracking Robot

ELG3336 "Following Flame" Object Tracking Robot

  • Order:
  • Duration: 2:50
  • Updated: 11 Jan 2013
  • views: 304
videos
ELG3336 Object Tracking Robot Project by Erika and Darik -goal was to create a low-budget interpretation of a person following robot that could be easily extended to a more useful product such as a luggage carrying robot
https://wn.com/Elg3336_Following_Flame_Object_Tracking_Robot
Augmented Reality Vuforia Extended Tracking Keep Object Even The Target Lost

Augmented Reality Vuforia Extended Tracking Keep Object Even The Target Lost

  • Order:
  • Duration: 4:15
  • Updated: 23 Sep 2017
  • views: 614
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
vuforia 3d object tracking

vuforia 3d object tracking

  • Order:
  • Duration: 1:26
  • Updated: 03 May 2017
  • views: 1688
videos
https://wn.com/Vuforia_3D_Object_Tracking
Object tracking with Sensor Fusion-based Extended Kalman Filter

Object tracking with Sensor Fusion-based Extended Kalman Filter

  • Order:
  • Duration: 0:20
  • Updated: 03 May 2017
  • views: 190
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
Vuforia Object tracking

Vuforia Object tracking

  • Order:
  • Duration: 0:14
  • Updated: 18 Jan 2017
  • views: 278
videos
https://wn.com/Vuforia_Object_Tracking
Object tracking using an extended kalman filter (EKF) with Lidar and Radar measurements

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

  • Order:
  • Duration: 0:12
  • Updated: 30 Jul 2017
  • views: 13
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. The C++ code can be found here "https://github.com/Heych88/udacity-sdcnd-extended-kalman-filter"
https://wn.com/Object_Tracking_Using_An_Extended_Kalman_Filter_(Ekf)_With_Lidar_And_Radar_Measurements
Extended Kalman Filter for object tracking

Extended Kalman Filter for object tracking

  • Order:
  • Duration: 0:36
  • Updated: 24 May 2017
  • views: 41
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 with Kalman filter (Java Programming language)

Object Tracking with Kalman filter (Java Programming language)

  • Order:
  • Duration: 4:26
  • Updated: 05 Jul 2013
  • views: 7073
videos
https://wn.com/Object_Tracking_With_Kalman_Filter_(Java_Programming_Language)
Directional Moving Object Tracking in 2D with the Extended Kalman Filter on Matrix Lie Groups

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

  • Order:
  • Duration: 2:37
  • Updated: 22 Sep 2016
  • views: 100
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
77 GHz Radar, Multiple object tracking, two people passing

77 GHz Radar, Multiple object tracking, two people passing

  • Order:
  • Duration: 0:11
  • Updated: 05 Jul 2016
  • views: 812
videos
High-end 77-GHz 4D radar system, Wide field-of-view, Unique algorithms to detect problematic slow-moving and stationary targets, Optional camera installation for reference and sensor fusion opportunities. Suitable for Autonomous Drive applications.
https://wn.com/77_Ghz_Radar,_Multiple_Object_Tracking,_Two_People_Passing
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