Facial Descriptors for Identity-Preserving Multiple People Tracking

In this report, we show that facial descriptors can be used very effectively in conjunction with a tracklet-based multi-person tracker both to localize and to identify or re-identify people over long sequences. Thus, we can reliably deliver both trajectories and identities in crowded scenes. Furthermore, the whole approach is fast enough to be implemented in real-time. Our key insight is that this can be done even though the faces can only be recognized relatively infrequently.

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

[2]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[3]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

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

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

[6]  Ramakant Nevatia,et al.  Multi-target tracking by online learning of non-linear motion patterns and robust appearance models , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Jonghyun Choi,et al.  Face Identification Using Large Feature Sets , 2012, IEEE Transactions on Image Processing.

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

[9]  Matej Kristan,et al.  Tracking by Identification Using Computer Vision and Radio , 2012, Sensors.

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

[11]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

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

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

[14]  Matti Pietikäinen,et al.  Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Jiri Matas,et al.  Face-TLD: Tracking-Learning-Detection applied to faces , 2010, 2010 IEEE International Conference on Image Processing.

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

[17]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  James J. Little,et al.  Identifying players in broadcast sports videos using conditional random fields , 2011, CVPR 2011.

[19]  Michail Zervos.  Multi-camera face detection and recognition applied to people tracking , 2013 .

[20]  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).

[21]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

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

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

[24]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[25]  Y. Freund,et al.  Active learning for visual object detection , 2005 .

[26]  Zhengyou Zhang,et al.  A Survey of Recent Advances in Face Detection , 2010 .

[27]  Luc Van Gool,et al.  Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[29]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[30]  Horesh Ben Shitrit.  Multi-camera face detection and recognition applied to people tracking , 2013 .

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

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

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

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

[35]  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.

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

[37]  Vladimir Pavlovic,et al.  An efficient IP approach to constrained multiple face tracking and recognition , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[38]  Sridha Sridharan,et al.  Automatic Tracking, Super-Resolution and Recognition of Human Faces from Surveillance Video , 2007, MVA.

[39]  Nuno Vasconcelos,et al.  Multiclass Boosting: Theory and Algorithms , 2011, NIPS.