• Discrete-Continuous Optimization for Multi-Target Tracking (CVPR 2012)

    Code available: http://goo.gl/rkKXN The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, \ie assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, \ie recovering the trajectories of all targets. To go beyond simple greedy solutions for data association, recent approaches often perform multi-target tracking using discrete optimization. This has the disadvantage that trajectories need to be pre-computed or represented discretely, thus limiting accuracy. In this paper we instead formulate multi-target tracking as a discrete-continuous problem that handles each aspect in its natural domain and allows one to leverage powerf...

    published: 01 Mar 2012
  • 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
  • Stable Multi-Target Tracking in Real-Time Surveillance Video (CVPR 2011)

    The video demonstrates a stable head tracking system that can run at 25fps on 1920x1080 video using a standard desktop computer. The system is capable of obtaining stable head images and is robust to temporary occlusions. For more information, see the following page: http://www.robots.ox.ac.uk/ActiveVision/Publications/benfold_reid_cvpr2011/benfold_reid_cvpr2011.html

    published: 21 Apr 2011
  • 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
  • Continuous Energy Minimization for Multi-Target Tracking

    A supplemental video for the following IEEE PAMI article Continuous Energy Minimization for Multitarget Tracking A. Milan, S. Roth and K. Schindler IEEE Trans. Pattern Anal. Mach. Intell. 36(1): 58-72 (2014)

    published: 08 Oct 2013
  • Multiple Target Tracking using Radar Detections - Part 2

    Radar sensor used : Continental ARS-308HSNO The program was run on the system whose specifications are: Intel Core i7 2620m, 12GiB RAM Algorithm used : Probabilistic Data Association Filter(PDAF) w/ some modifications & devised track management scheme

    published: 13 Sep 2016
  • Multiple Target Tracking using Radar Detections

    Radar sensor used : Continental ARS-308HSNO The program was run on the system whose specifications are: Intel Core i7 2620m, 8GiB RAM Algorithm used : Probabilistic Data Association Filter(PDAF) w/ some modifications & devised track management scheme

    published: 26 Aug 2016
  • Augmented Reality Tutorial Multiple Target Tracking with Wikitude

    Get a free Wikitude SDK7 software here: http://bit.ly/WikitudeDownloadSDK Tutorial assets provided here: https://www.ourtechart.com/augmented-reality/tutorial/ar-card-game/ You can always find some other types of ninjas in Unity Asset Store: Kunoichi - Ninja Character: https://goo.gl/1fnrxX Jade Ninja - Mobile: https://goo.gl/wmGJ3C AR blog: https://www.ourtechart.com Follow me on: Facebook ► https://facebook.com/EdgarasArt Twitter ► https://twitter.com/EdgarasArt Instagram ► https://instagram.com/EdgarasArt Song: Valley of the Springs Album: The Tea House Year: 2017 Music Copyright © Brandon Fiechter

    published: 04 Aug 2017
  • OpenCV 3 Multiple Object Tracking by Image Subtraction C++ full source code

    If you found this video helpful please consider supporting me on Patreon: https://www.patreon.com/18F4550videos?ty=h Prerequisite: OpenCV C++ Installation/Configuration: YouTube video: https://www.youtube.com/watch?v=7SM5OD2pZKY GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Windows_10_Installation_Tutorial Part 1: OpenCV 3 Play Video File C++ YouTube video: https://www.youtube.com/watch?v=VE-1UarzE40 GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Play_Video_File_Cpp Part 2: OpenCV 3 Image Subtraction C++ YouTube video: https://www.youtube.com/watch?v=5SzNuPSaISM GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Image_Subtraction_Cpp Part 3: OpenCV 3 Mouse Move Prediction Algorithm C++ YouTube video: https://www.youtu...

    published: 14 Feb 2016
  • Multi Target Tracking

    ATracker is a tracker based on the computation of costs. It is a multi-target tracker, which is able to manage groups. Code is available here: https://github.com/apennisi/multi_target_tracker/blob/master/README.md

    published: 23 May 2017
  • Multi-target Tracking by Continuous Energy Minimization (CVPR 2011)

    This video file shows the global optimization for one particular time window followed by two example sequences.

    published: 29 Nov 2011
  • Target Identity-aware Network Flow for Online Multiple Target Tracking

    Afshin Dehghan presents TINF tracker Authors: Afshin Dehghan, Yicong Tian, Philip. H. S. Torr, Mubarak Shah, in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2015) homepage:http://crcv.ucf.edu/people/phd_students/afshin/

    published: 04 Jun 2015
  • Occlusion Handling in Multiple Target Detection and Tracking

    Real-time Multiple target tracking by Detection with Occlusion Handling - PETS2009 Scenario (The method is filed with USPTO on May 2015)

    published: 08 Jun 2015
  • MULTIPLE TARGET CAMERA in Unity

    Get all Udemy courses for only $10: http://bit.ly/BRACKEYSDEC The link above is an exclusive limited $10 site wide deal that expires by end of December so... go nuts! Learn how to make a Camera that follows Multiple Targets using Unity. ♥ Support Brackeys on Patreon: http://patreon.com/brackeys/ ···················································································· ♥ Donate: http://brackeys.com/donate/ ♥ Subscribe: http://bit.ly/1kMekJV ● Website: http://brackeys.com/ ● Facebook: https://facebook.com/brackeys/ ● Twitter: https://twitter.com/BrackeysTweet/ ········································­­·······································­·­···· Edited by the lovely Sofibab. ········································­­·······································­·­···· ► All ...

    published: 17 Dec 2017
  • Multi-Class Multi-Object Tracking using Changing Point Detection

    Multiple Object Tracking Benchmark 2016 (MOT 2016) Multi-Class Multi-Object Tracking using Changing Point Detection Byungjae Lee, Enkhbayar Erdenee, Songguo Jin, Mi Young Nam, Phill Kyu Rhee https://arxiv.org/abs/1608.08434

    published: 28 Jul 2016
  • Multiple object tracking (MOT) paradigm in EventIDE

    Template for the famous MOT paradigm (Pylyshyn&Storm, 1998 Scholl&Pylyshyn, 1999) is added to the EventIDE template gallery. The template contains a code generating unpredictable trajectories for occlusion-free motion of multiple objects. Trajectory generation is a random process controlled by several initial parameters that can be customized. In addition, the template demonstrates how to use recurrent events to create a time-precise animation with EventIDE. This video shows a single MOT trial with 10 moving objects and 4 tracking targets.

    published: 12 May 2012
  • Multi-Camera Multi-Target Tracking (BMVC 2015)

    Efficient Spatio-Temporal Data Association Using Multidimensional Assignment for Multi-Camera Multi-Target Tracking (BMVC 2015) This paper proposes a novel multi-target tracking method which jointly solves a data association problem using images from multiple cameras. In this work, the spatiotemporal data association problem is formulated as a multidimensional assignment problem (MDA). To achieve a fast, efficient, and easily implementable approximation algorithm, we solve the MDA problem approximately by solving a sequence of bipartite matching problems using random splitting and merging operations. In this formulation, we design a new cost function, considering the accuracy in 3D reconstruction, motion smoothness, visibility from cameras, starting/ending at entrance and exit zone, and f...

    published: 28 Sep 2016
  • ATI's Multi-Target Tracking and Multi-Sensor Data Fusion Technical Training Seminar sampler video 2

    What you will learn: State Estimation Techniques Kalman Filter, constant-gain filters. Non-linear filtering When is it needed? Extended Kalman Filter. Techniques for angle-only tracking. Tracking algorithms, their advantages and limitations, including: - Nearest Neighbor - Probabilistic Data Association - Multiple Hypothesis Tracking - Interactive Multiple Model (IMM) How to handle maneuvering targets. Track initiation recursive and batch approaches. Architectures for sensor fusion. Sensor alignment Why do we need it and how do we do it? Attribute Fusion, including Bayesian methods, Dempster-Shafer, Fuzzy Logic.

    published: 14 May 2010
  • Global Data Association for Multiple Pedestrian Tracking

    Final Oral Examination of: Afshin Dehghan For the Degree of: Doctor of Philosophy (Computer Science) Firstly, a new framework for multi-target tracking that uses a novel data association technique employing the Generalized Maximum Clique Problem (GMCP) formulation is presented. The majority of current methods, such as bipartite matching, incorporate a limited temporal locality of the sequence into the data association problem. On the other hand, our approach incorporates both motion and appearance in a global manner. The proposed method incorporates the whole temporal span and solves the data association problem for one object at a time. GMCP is used to solve the optimization problem of our data association. GMCP leads us to a more accurate approach to multi-object tracking; however, it...

    published: 26 Apr 2016
  • Deep Learning for Multi-Target Tracking

    published: 27 Apr 2016
  • OpenCV Tutorial: Multiple Object Tracking in Real Time (1/3)

    Found this video useful? Donations are very much appreciated, thank you. PayPal: https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=X24GRDPJ4PZHW&lc=CA&item_name=OpenCV%20Tutorials¤cy_code=CAD&bn=PP%2dDonationsBF%3abtn_donateCC_LG%2egif%3aNonHosted BTC: 18Hysn4veDCCkhKtkqBiigJ8HfhjkzWDta Ethereum: 0x97267a8d15d35012FaA9B07be4ac5Ff935876E10 Business Inquiries and Tutoring rates email kyle.hounslow@gmail.com This is a continuation of "Tutorial: Real-Time Object Tracking Using OpenCV" http://www.youtube.com/watch?v=bSeFrPrqZ2A where we extend the code to classify and track multiple objects from a webcam stream. File #1 (right-click, Save link as): https://raw.githubusercontent.com/kylehounslow/opencv-tuts/master/multiple-object-tracking-tut/part-one/multipleObjectTrackin...

    published: 07 Aug 2013
  • Multiple Object Tracking - Highway

    SAP Internship 2016 Application was implemented in Python, with usage of OpenCV

    published: 20 Nov 2016
  • Multiple target tracking using multiple UAVs

    published: 10 Jul 2016
  • Multiple Moving Target Tracking [Part 2]

    Part 1: https://youtu.be/PP7wXdcLZHo Blob detection algorithm using Kalman Filter objects to predict blob positions and track across successive. The algorithm is capable of tracking through long-term occlusion with some success. It has been designed for the tracking of small, fast-moving objects such as bats. This is an improved version of the algorithm used in Part 1 with several changes made to the tracking logic. Results are shown for a number of different motion paths, object types and lighting conditions. Using OpenCV 3.1 and MS Visual Studio 2015. Source code: https://github.com/ToastyThePenguin/MultiTracker Colour and Infrared datasets: https://drive.google.com/open?id=0BymGrxTSHpScYjRkY3Z6M1R1MTA

    published: 10 Nov 2016
developed with YouTube
Discrete-Continuous Optimization for Multi-Target Tracking (CVPR 2012)
2:38

Discrete-Continuous Optimization for Multi-Target Tracking (CVPR 2012)

  • Order:
  • Duration: 2:38
  • Updated: 01 Mar 2012
  • views: 69899
videos
Code available: http://goo.gl/rkKXN The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, \ie assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, \ie recovering the trajectories of all targets. To go beyond simple greedy solutions for data association, recent approaches often perform multi-target tracking using discrete optimization. This has the disadvantage that trajectories need to be pre-computed or represented discretely, thus limiting accuracy. In this paper we instead formulate multi-target tracking as a discrete-continuous problem that handles each aspect in its natural domain and allows one to leverage powerful algorithms for multi-model fitting. Data association is performed using discrete optimization with label costs, and can be solved to near optimality. Trajectory estimation is posed as a continuous fitting problem with a simple closed-form solution, which is used in turn to update the label costs. We demonstrate the accuracy and robustness of our approach with state-of-the-art performance on several standard datasets.
https://wn.com/Discrete_Continuous_Optimization_For_Multi_Target_Tracking_(Cvpr_2012)
Multi Object Tracking Tutorial: part 1  by Student Dave
9:46

Multi Object Tracking Tutorial: part 1 by Student Dave

  • Order:
  • Duration: 9:46
  • Updated: 30 Jan 2013
  • views: 18753
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
Stable Multi-Target Tracking in Real-Time Surveillance Video (CVPR 2011)
1:11

Stable Multi-Target Tracking in Real-Time Surveillance Video (CVPR 2011)

  • Order:
  • Duration: 1:11
  • Updated: 21 Apr 2011
  • views: 157740
videos
The video demonstrates a stable head tracking system that can run at 25fps on 1920x1080 video using a standard desktop computer. The system is capable of obtaining stable head images and is robust to temporary occlusions. For more information, see the following page: http://www.robots.ox.ac.uk/ActiveVision/Publications/benfold_reid_cvpr2011/benfold_reid_cvpr2011.html
https://wn.com/Stable_Multi_Target_Tracking_In_Real_Time_Surveillance_Video_(Cvpr_2011)
Multiple object tracking with kalman tracker and sort
3:01

Multiple object tracking with kalman tracker and sort

  • Order:
  • Duration: 3:01
  • Updated: 24 Jul 2017
  • views: 5468
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
Continuous Energy Minimization for Multi-Target Tracking
1:42

Continuous Energy Minimization for Multi-Target Tracking

  • Order:
  • Duration: 1:42
  • Updated: 08 Oct 2013
  • views: 2167
videos
A supplemental video for the following IEEE PAMI article Continuous Energy Minimization for Multitarget Tracking A. Milan, S. Roth and K. Schindler IEEE Trans. Pattern Anal. Mach. Intell. 36(1): 58-72 (2014)
https://wn.com/Continuous_Energy_Minimization_For_Multi_Target_Tracking
Multiple Target Tracking using Radar Detections - Part 2
27:25

Multiple Target Tracking using Radar Detections - Part 2

  • Order:
  • Duration: 27:25
  • Updated: 13 Sep 2016
  • views: 1060
videos
Radar sensor used : Continental ARS-308HSNO The program was run on the system whose specifications are: Intel Core i7 2620m, 12GiB RAM Algorithm used : Probabilistic Data Association Filter(PDAF) w/ some modifications & devised track management scheme
https://wn.com/Multiple_Target_Tracking_Using_Radar_Detections_Part_2
Multiple Target Tracking using Radar Detections
9:09

Multiple Target Tracking using Radar Detections

  • Order:
  • Duration: 9:09
  • Updated: 26 Aug 2016
  • views: 734
videos
Radar sensor used : Continental ARS-308HSNO The program was run on the system whose specifications are: Intel Core i7 2620m, 8GiB RAM Algorithm used : Probabilistic Data Association Filter(PDAF) w/ some modifications & devised track management scheme
https://wn.com/Multiple_Target_Tracking_Using_Radar_Detections
Augmented Reality Tutorial Multiple Target Tracking with Wikitude
17:31

Augmented Reality Tutorial Multiple Target Tracking with Wikitude

  • Order:
  • Duration: 17:31
  • Updated: 04 Aug 2017
  • views: 8036
videos
Get a free Wikitude SDK7 software here: http://bit.ly/WikitudeDownloadSDK Tutorial assets provided here: https://www.ourtechart.com/augmented-reality/tutorial/ar-card-game/ You can always find some other types of ninjas in Unity Asset Store: Kunoichi - Ninja Character: https://goo.gl/1fnrxX Jade Ninja - Mobile: https://goo.gl/wmGJ3C AR blog: https://www.ourtechart.com Follow me on: Facebook ► https://facebook.com/EdgarasArt Twitter ► https://twitter.com/EdgarasArt Instagram ► https://instagram.com/EdgarasArt Song: Valley of the Springs Album: The Tea House Year: 2017 Music Copyright © Brandon Fiechter
https://wn.com/Augmented_Reality_Tutorial_Multiple_Target_Tracking_With_Wikitude
OpenCV 3 Multiple Object Tracking by Image Subtraction C++ full source code
13:55

OpenCV 3 Multiple Object Tracking by Image Subtraction C++ full source code

  • Order:
  • Duration: 13:55
  • Updated: 14 Feb 2016
  • views: 28652
videos
If you found this video helpful please consider supporting me on Patreon: https://www.patreon.com/18F4550videos?ty=h Prerequisite: OpenCV C++ Installation/Configuration: YouTube video: https://www.youtube.com/watch?v=7SM5OD2pZKY GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Windows_10_Installation_Tutorial Part 1: OpenCV 3 Play Video File C++ YouTube video: https://www.youtube.com/watch?v=VE-1UarzE40 GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Play_Video_File_Cpp Part 2: OpenCV 3 Image Subtraction C++ YouTube video: https://www.youtube.com/watch?v=5SzNuPSaISM GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Image_Subtraction_Cpp Part 3: OpenCV 3 Mouse Move Prediction Algorithm C++ YouTube video: https://www.youtube.com/watch?v=Tbcn7XTXunA GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Mouse_Move_Prediction_Cpp Part 4: OpenCV 3 Multiple Object Tracking by Image Subtraction C++ full source code YouTube video: https://www.youtube.com/watch?v=A4UDOAOTRdw GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Multiple_Object_Tracking_by_Image_Subtraction_Cpp Part 5: OpenCV 3 Car Counting C++ full source code YouTube video: https://www.youtube.com/watch?v=Y3ac5rFMNZ0 GitHub repository: https://github.com/MicrocontrollersAndMore/OpenCV_3_Car_Counting_Cpp
https://wn.com/Opencv_3_Multiple_Object_Tracking_By_Image_Subtraction_C_Full_Source_Code
Multi Target Tracking
1:54

Multi Target Tracking

  • Order:
  • Duration: 1:54
  • Updated: 23 May 2017
  • views: 646
videos
ATracker is a tracker based on the computation of costs. It is a multi-target tracker, which is able to manage groups. Code is available here: https://github.com/apennisi/multi_target_tracker/blob/master/README.md
https://wn.com/Multi_Target_Tracking
Multi-target Tracking by Continuous Energy Minimization (CVPR 2011)
2:49

Multi-target Tracking by Continuous Energy Minimization (CVPR 2011)

  • Order:
  • Duration: 2:49
  • Updated: 29 Nov 2011
  • views: 12762
videos
This video file shows the global optimization for one particular time window followed by two example sequences.
https://wn.com/Multi_Target_Tracking_By_Continuous_Energy_Minimization_(Cvpr_2011)
Target Identity-aware Network Flow for Online Multiple Target Tracking
18:46

Target Identity-aware Network Flow for Online Multiple Target Tracking

  • Order:
  • Duration: 18:46
  • Updated: 04 Jun 2015
  • views: 2093
videos
Afshin Dehghan presents TINF tracker Authors: Afshin Dehghan, Yicong Tian, Philip. H. S. Torr, Mubarak Shah, in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2015) homepage:http://crcv.ucf.edu/people/phd_students/afshin/
https://wn.com/Target_Identity_Aware_Network_Flow_For_Online_Multiple_Target_Tracking
Occlusion Handling in Multiple Target Detection and Tracking
0:42

Occlusion Handling in Multiple Target Detection and Tracking

  • Order:
  • Duration: 0:42
  • Updated: 08 Jun 2015
  • views: 243
videos
Real-time Multiple target tracking by Detection with Occlusion Handling - PETS2009 Scenario (The method is filed with USPTO on May 2015)
https://wn.com/Occlusion_Handling_In_Multiple_Target_Detection_And_Tracking
MULTIPLE TARGET CAMERA in Unity
12:55

MULTIPLE TARGET CAMERA in Unity

  • Order:
  • Duration: 12:55
  • Updated: 17 Dec 2017
  • views: 46372
videos
Get all Udemy courses for only $10: http://bit.ly/BRACKEYSDEC The link above is an exclusive limited $10 site wide deal that expires by end of December so... go nuts! Learn how to make a Camera that follows Multiple Targets using Unity. ♥ Support Brackeys on Patreon: http://patreon.com/brackeys/ ···················································································· ♥ Donate: http://brackeys.com/donate/ ♥ Subscribe: http://bit.ly/1kMekJV ● Website: http://brackeys.com/ ● Facebook: https://facebook.com/brackeys/ ● Twitter: https://twitter.com/BrackeysTweet/ ········································­­·······································­·­···· Edited by the lovely Sofibab. ········································­­·······································­·­···· ► All content by Brackeys is 100% free. We believe that education should be available for everyone. Any support is truly appreciated so we can keep on making the content free of charge. ········································­­·······································­·­···· ♪ Baby Plays Electro Games http://teknoaxe.com/cgi-bin/link_code_2.pl?326
https://wn.com/Multiple_Target_Camera_In_Unity
Multi-Class Multi-Object Tracking using Changing Point Detection
1:03

Multi-Class Multi-Object Tracking using Changing Point Detection

  • Order:
  • Duration: 1:03
  • Updated: 28 Jul 2016
  • views: 3895
videos
Multiple Object Tracking Benchmark 2016 (MOT 2016) Multi-Class Multi-Object Tracking using Changing Point Detection Byungjae Lee, Enkhbayar Erdenee, Songguo Jin, Mi Young Nam, Phill Kyu Rhee https://arxiv.org/abs/1608.08434
https://wn.com/Multi_Class_Multi_Object_Tracking_Using_Changing_Point_Detection
Multiple object tracking (MOT) paradigm in EventIDE
0:16

Multiple object tracking (MOT) paradigm in EventIDE

  • Order:
  • Duration: 0:16
  • Updated: 12 May 2012
  • views: 10302
videos
Template for the famous MOT paradigm (Pylyshyn&Storm, 1998 Scholl&Pylyshyn, 1999) is added to the EventIDE template gallery. The template contains a code generating unpredictable trajectories for occlusion-free motion of multiple objects. Trajectory generation is a random process controlled by several initial parameters that can be customized. In addition, the template demonstrates how to use recurrent events to create a time-precise animation with EventIDE. This video shows a single MOT trial with 10 moving objects and 4 tracking targets.
https://wn.com/Multiple_Object_Tracking_(Mot)_Paradigm_In_Eventide
Multi-Camera Multi-Target Tracking (BMVC 2015)
2:28

Multi-Camera Multi-Target Tracking (BMVC 2015)

  • Order:
  • Duration: 2:28
  • Updated: 28 Sep 2016
  • views: 1398
videos
Efficient Spatio-Temporal Data Association Using Multidimensional Assignment for Multi-Camera Multi-Target Tracking (BMVC 2015) This paper proposes a novel multi-target tracking method which jointly solves a data association problem using images from multiple cameras. In this work, the spatiotemporal data association problem is formulated as a multidimensional assignment problem (MDA). To achieve a fast, efficient, and easily implementable approximation algorithm, we solve the MDA problem approximately by solving a sequence of bipartite matching problems using random splitting and merging operations. In this formulation, we design a new cost function, considering the accuracy in 3D reconstruction, motion smoothness, visibility from cameras, starting/ending at entrance and exit zone, and false positive. Our approach reconstructs 3D trajectories that represent people’s movement as 3D cylinders whose locations are estimated considering all adjacent frames. The experiments illustrate the proposed method shows the state-of-the-art performance in challenging multi-camera datasets and the computational efficiency with 8 times faster computation than the existing BIP approach.
https://wn.com/Multi_Camera_Multi_Target_Tracking_(Bmvc_2015)
ATI's Multi-Target Tracking and Multi-Sensor Data Fusion Technical Training Seminar  sampler video 2
4:35

ATI's Multi-Target Tracking and Multi-Sensor Data Fusion Technical Training Seminar sampler video 2

  • Order:
  • Duration: 4:35
  • Updated: 14 May 2010
  • views: 3152
videos
What you will learn: State Estimation Techniques Kalman Filter, constant-gain filters. Non-linear filtering When is it needed? Extended Kalman Filter. Techniques for angle-only tracking. Tracking algorithms, their advantages and limitations, including: - Nearest Neighbor - Probabilistic Data Association - Multiple Hypothesis Tracking - Interactive Multiple Model (IMM) How to handle maneuvering targets. Track initiation recursive and batch approaches. Architectures for sensor fusion. Sensor alignment Why do we need it and how do we do it? Attribute Fusion, including Bayesian methods, Dempster-Shafer, Fuzzy Logic.
https://wn.com/Ati's_Multi_Target_Tracking_And_Multi_Sensor_Data_Fusion_Technical_Training_Seminar_Sampler_Video_2
Global Data Association for Multiple Pedestrian Tracking
44:17

Global Data Association for Multiple Pedestrian Tracking

  • Order:
  • Duration: 44:17
  • Updated: 26 Apr 2016
  • views: 4423
videos
Final Oral Examination of: Afshin Dehghan For the Degree of: Doctor of Philosophy (Computer Science) Firstly, a new framework for multi-target tracking that uses a novel data association technique employing the Generalized Maximum Clique Problem (GMCP) formulation is presented. The majority of current methods, such as bipartite matching, incorporate a limited temporal locality of the sequence into the data association problem. On the other hand, our approach incorporates both motion and appearance in a global manner. The proposed method incorporates the whole temporal span and solves the data association problem for one object at a time. GMCP is used to solve the optimization problem of our data association. GMCP leads us to a more accurate approach to multi-object tracking; however, it has some limitations. Firstly, it finds target trajectories one-by-one, missing joint optimization. Secondly, for optimization we use a greedy solver, making GMCP prone to local minima. Finally GMCP tracker is slow. In order to address these problems, we propose a new graph theoretic problem formulation called Generalized Maximum Multi Clique Problem (GMMCP). GMMCP has all the advantages of the GMCP tracker while addressing its limitations. Previous works assume simplified version of the ideal tracking scenario either in problem formulation or problem optimization. However, we propose a solution to GMMCP where no simplification is assumed in either steps. We show that, GMMCP can be solved efficiently through Binary-Integer Program while guaranteeing the optimal solution. We further propose a speed-up method which reduces the size of input graph without assuming any heuristic. Thus far we have assumed that the number of people do not exceed a few dozen. However, this is not always the case. In many scenarios such as, marathons, political rallies or religious rites, the number of people in a frame may reach few hundreds or even few thousands. Human detection methods often fail to localize objects in extremely crowded scenes. This limits the use of data association based tracking methods, including GMCP and GMMCP. Finally, we formulate online crowd tracking as a Binary Quadratic Programing, where both detection and data association problems are solved together. Our tracker brings in both target’s individual information and contextual cues into a single objective function. Due to large number of targets, state-of-the-art commercial quadratic programing solvers fail to efficiently find the solution to proposed optimization. In order to overcome the computational complexity of available solvers, we propose to use the most recent version of Modified Frank-Wolfe algorithm. The proposed tracker can track hundreds of targets efficiently and improve state-of-the-art results by significant margin.
https://wn.com/Global_Data_Association_For_Multiple_Pedestrian_Tracking
Deep Learning for Multi-Target Tracking
0:55

Deep Learning for Multi-Target Tracking

  • Order:
  • Duration: 0:55
  • Updated: 27 Apr 2016
  • views: 975
videos
https://wn.com/Deep_Learning_For_Multi_Target_Tracking
OpenCV Tutorial: Multiple Object Tracking in Real Time (1/3)
16:56

OpenCV Tutorial: Multiple Object Tracking in Real Time (1/3)

  • Order:
  • Duration: 16:56
  • Updated: 07 Aug 2013
  • views: 130345
videos
Found this video useful? Donations are very much appreciated, thank you. PayPal: https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=X24GRDPJ4PZHW&lc=CA&item_name=OpenCV%20Tutorials¤cy_code=CAD&bn=PP%2dDonationsBF%3abtn_donateCC_LG%2egif%3aNonHosted BTC: 18Hysn4veDCCkhKtkqBiigJ8HfhjkzWDta Ethereum: 0x97267a8d15d35012FaA9B07be4ac5Ff935876E10 Business Inquiries and Tutoring rates email kyle.hounslow@gmail.com This is a continuation of "Tutorial: Real-Time Object Tracking Using OpenCV" http://www.youtube.com/watch?v=bSeFrPrqZ2A where we extend the code to classify and track multiple objects from a webcam stream. File #1 (right-click, Save link as): https://raw.githubusercontent.com/kylehounslow/opencv-tuts/master/multiple-object-tracking-tut/part-one/multipleObjectTracking.cpp
https://wn.com/Opencv_Tutorial_Multiple_Object_Tracking_In_Real_Time_(1_3)
Multiple Object Tracking - Highway
1:22

Multiple Object Tracking - Highway

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  • Duration: 1:22
  • Updated: 20 Nov 2016
  • views: 705
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SAP Internship 2016 Application was implemented in Python, with usage of OpenCV
https://wn.com/Multiple_Object_Tracking_Highway
Multiple target tracking using multiple UAVs
0:38

Multiple target tracking using multiple UAVs

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  • Duration: 0:38
  • Updated: 10 Jul 2016
  • views: 133
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https://wn.com/Multiple_Target_Tracking_Using_Multiple_Uavs
Multiple Moving Target Tracking [Part 2]
8:39

Multiple Moving Target Tracking [Part 2]

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  • Duration: 8:39
  • Updated: 10 Nov 2016
  • views: 691
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Part 1: https://youtu.be/PP7wXdcLZHo Blob detection algorithm using Kalman Filter objects to predict blob positions and track across successive. The algorithm is capable of tracking through long-term occlusion with some success. It has been designed for the tracking of small, fast-moving objects such as bats. This is an improved version of the algorithm used in Part 1 with several changes made to the tracking logic. Results are shown for a number of different motion paths, object types and lighting conditions. Using OpenCV 3.1 and MS Visual Studio 2015. Source code: https://github.com/ToastyThePenguin/MultiTracker Colour and Infrared datasets: https://drive.google.com/open?id=0BymGrxTSHpScYjRkY3Z6M1R1MTA
https://wn.com/Multiple_Moving_Target_Tracking_Part_2
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