Online multi-person tracking via robust collaborative model

The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a model for collaboration between a pre-trained object detector and multiple single object trackers in the particle filter tracking framework. For each frame, we construct an association between the trackers and the detections, and when a tracker is successfully associated to a detection, we treat this detection as the key-sample for this tracker. We present a dual motion model that incorporates the associated detections with the object dynamics. Then, a likelihood function provides different weights for the propagated and the newly created particles, reducing the effect of false positives and missed detections in the tracking process. In addition, we use generative and discriminative appearance models to maximize the appearance variation among the targets. The performance of the proposed algorithm compares favorably with that of the state-of-the-art approaches on three public sequences.

[1]  Volker Eiselein,et al.  Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[2]  Afshin Dehghan,et al.  GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs , 2012, ECCV.

[3]  Patrick Pérez,et al.  Maintaining multimodality through mixture tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Yong Rui,et al.  Better proposal distributions: object tracking using unscented particle filter , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Pietro Perona,et al.  The Fastest Pedestrian Detector in the West , 2010, BMVC.

[6]  Tang Yongli,et al.  Research on Particle Filter Based on an Improved Hybrid Proposal Distribution with Adaptive Parameter Optimization , 2012, 2012 Fifth International Conference on Intelligent Computation Technology and Automation.

[7]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[8]  David Gerónimo Gómez,et al.  State-Driven Particle Filter for Multi-person Tracking , 2012, ACIVS.

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

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

[11]  Petar M. Djuric,et al.  A hybrid importance function for particle filtering , 2004, IEEE Signal Processing Letters.

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

[13]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[14]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[16]  R. Mahler.  Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[17]  Jian Yang,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Afshin Dehghan,et al.  Part-based multiple-person tracking with partial occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Ramakant Nevatia,et al.  Multi-target tracking by on-line learned discriminative appearance models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  B. S. Manjunath,et al.  Robust multiple object tracking by detection with interacting Markov chain Monte Carlo , 2013, 2013 IEEE International Conference on Image Processing.

[22]  Mohamed R. Amer,et al.  Multiobject tracking as maximum weight independent set , 2011, CVPR 2011.

[23]  Emilio Maggio,et al.  Particle PHD Filtering for Multi-Target Visual Tracking , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[24]  Hua Han,et al.  An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking , 2011, Comput. Math. Appl..

[25]  Hanqing Lu,et al.  Boosted Interactively Distributed Particle Filter for automatic multi-object tracking , 2008, 2008 15th IEEE International Conference on Image Processing.

[26]  Stan Sclaroff,et al.  Online Multi-person Tracking by Tracker Hierarchy , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[27]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.