Re-identification for Improved People Tracking

Re-identification is usually defined as the problem of deciding whether a person currently in the field of view of a camera has been seen earlier either by that camera or another. However, a different version of the problem arises even when people are seen by multiple cameras with overlapping fields of view. Current tracking algorithms can easily get confused when people come close to each other and merge trajectory fragments into trajectories that include erroneous identity switches. Preventing this means re-identifying people across trajectory fragments. In this chapter, we show that this can be done very effectively by formulating the problem as a minimum-cost maximum-flow linear program. This version of the re-identification problem can be solved in real-time and produces trajectories without identity switches. We demonstrate the power of our approach both in single- and multicamera setups to track pedestrians, soccer players, and basketball players.

[1]  Charless C. Fowlkes,et al.  Globally-optimal greedy algorithms for tracking a variable number of objects , 2011, CVPR 2011.

[2]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[5]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[6]  Narendra Karmarkar,et al.  A new polynomial-time algorithm for linear programming , 1984, Comb..

[7]  James J. Little,et al.  A Linear Programming Approach for Multiple Object Tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Nobuyuki Yagi,et al.  Probabilistic Integration of Tracking and Recognition of Soccer Players , 2009, MMM.

[9]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Pascal Fua,et al.  Tracking multiple people under global appearance constraints , 2011, 2011 International Conference on Computer Vision.

[11]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Alberto Del Bimbo,et al.  Person Detection Using Temporal and Geometric Context with a Pan Tilt Zoom Camera , 2010, 2010 20th International Conference on Pattern Recognition.

[13]  Ramakant Nevatia,et al.  Human detection by searching in 3d space using camera and scene knowledge , 2008, 2008 19th International Conference on Pattern Recognition.

[14]  Bruno Steux,et al.  YEF (Yet Even Faster) Real-Time Object Detection , 2005, ALaRT.

[15]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[16]  Pascal Fua,et al.  Multi-Commodity Network Flow for Tracking Multiple People , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Hanif D. Sherali,et al.  Linear Programming and Network Flows: Bazaraa/Linear , 2009 .

[18]  Frits C. R. Spieksma,et al.  An LP-based algorithm for the data association problem in multitarget tracking , 2003, Comput. Oper. Res..

[19]  Stefan Roth,et al.  People-tracking-by-detection and people-detection-by-tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Nesa L'abbe Wu,et al.  Linear programming and extensions , 1981 .

[21]  Konrad Schindler,et al.  Globally Optimal Multi-target Tracking on a Hexagonal Lattice , 2010, ECCV.

[22]  J. W. Suuballe,et al.  Disjoint Paths in a Network , 2022 .

[23]  Konrad Schindler,et al.  Discrete-continuous optimization for multi-target tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[25]  Bruno Steux,et al.  YEF∗Real-Time Object Detection , 2004 .

[26]  Pushmeet Kohli,et al.  On Detection of Multiple Object Instances Using Hough Transforms , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Tiziana D'Orazio,et al.  A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[28]  A. G. Amitha Perera,et al.  Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[29]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[30]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[31]  Pascal Fua,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiple Object Tracking Using K-shortest Paths Optimization , 2022 .

[32]  Hanif D. Sherali,et al.  Linear Programming and Network Flows , 1977 .

[33]  Bernt Schiele,et al.  Monocular 3D pose estimation and tracking by detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[34]  A. Ellis,et al.  PETS2009 and Winter-PETS 2009 results: A combined evaluation , 2009, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance.

[35]  Deva Ramanan,et al.  Steerable part models , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Pascal Fua,et al.  Facial Descriptors for Identity-Preserving Multiple People Tracking , 2013 .

[37]  James Ferryman,et al.  Proceedings of the thirteenth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance , 2009 .

[38]  Ramakant Nevatia,et al.  Global data association for multi-object tracking using network flows , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.