Tracking Multiple Persons Based on a Variational Bayesian Model

Object tracking is an ubiquitous problem in computer vision with many applications in human-machine and human-robot interaction, augmented reality, driving assistance, surveillance, etc. Although thoroughly investigated, tracking multiple persons remains a challenging and an open problem. In this paper, an online variational Bayesian model for multiple-person tracking is proposed. This yields a variational expectation-maximization (VEM) algorithm. The computational efficiency of the proposed method is due to closed-form expressions for both the posterior distributions of the latent variables and for the estimation of the model parameters. A stochastic process that handles person birth and person death enables the tracker to handle a varying number of persons over long periods of time. The proposed method is benchmarked using the MOT 2016 dataset.

[1]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[2]  Ronald Mahler,et al.  Multisource multitarget filtering: a unified approach , 1998, Defense, Security, and Sensing.

[3]  P. Green,et al.  Trans-dimensional Markov chain Monte Carlo , 2000 .

[4]  R. Mahler.  A Theoretical Foundation for the Stein-Winter "Probability Hypothesis Density (PHD)" Multitarget Tracking Approach , 2000 .

[5]  Patrick Pérez,et al.  Variational inference for visual tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  Sumeetpal S. Singh,et al.  Random finite sets and sequential Monte Carlo methods in multi-target tracking , 2003, 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695).

[7]  Hedvig Kjellström,et al.  Multi-target particle filtering for the probability hypothesis density , 2003, ArXiv.

[8]  R.P.S. Mahler,et al.  "Statistics 101" for multisensor, multitarget data fusion , 2004, IEEE Aerospace and Electronic Systems Magazine.

[9]  Frank Dellaert,et al.  An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets , 2004, ECCV.

[10]  Jean-Marc Odobez,et al.  Using particles to track varying numbers of interacting people , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Jean-Marc Odobez,et al.  Evaluating Multi-Object Tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[12]  V. Šmídl,et al.  The Variational Bayes Method in Signal Processing , 2005 .

[13]  Daniel E. Clark,et al.  Convergence results for the particle PHD filter , 2006, IEEE Transactions on Signal Processing.

[14]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[15]  Rainer Stiefelhagen,et al.  The CLEAR 2006 Evaluation , 2006, CLEAR.

[16]  Ba-Ngu Vo,et al.  Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach , 2006, IEEE Transactions on Signal Processing.

[17]  Emilio Maggio,et al.  Efficient Multitarget Visual Tracking Using Random Finite Sets , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Y. Bar-Shalom,et al.  The probabilistic data association filter , 2009, IEEE Control Systems.

[19]  Ba-Ngu Vo,et al.  Performance evaluation of multi-target tracking using the OSPA metric , 2010, 2010 13th International Conference on Information Fusion.

[20]  Ramakant Nevatia,et al.  An online learned CRF model for multi-target tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Wenhan Luo,et al.  Multiple Object Tracking: A Review , 2014, ArXiv.

[22]  Alexandre Heili,et al.  Exploiting Long-Term Connectivity and Visual Motion in CRF-Based Multi-Person Tracking , 2014, IEEE Transactions on Image Processing.

[23]  Stan Z. Li,et al.  A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Konrad Schindler,et al.  Continuous Energy Minimization for Multitarget Tracking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Stefan Roth,et al.  MOT16: A Benchmark for Multi-Object Tracking , 2016, ArXiv.

[26]  Radu Horaud,et al.  An on-line variational Bayesian model for multi-person tracking from cluttered scenes , 2016, Comput. Vis. Image Underst..