A data association algorithm for multiple object tracking in video sequences

This paper presents a particle filtering algorithm for multiple object tracking. The proposed particle filter (PF) embeds a data association technique based on the joint probabilistic data association (JPDA) which handles the uncertainty of the measurement origin.

[1]  D. Koller,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[2]  J. Vermaak,et al.  A review of recent results in multiple target tracking , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[3]  Luc Van Gool,et al.  An adaptive color-based particle filter , 2003, Image Vis. Comput..

[4]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[6]  Fredrik Gustafsson,et al.  Monte Carlo data association for multiple target tracking , 2001 .

[7]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[8]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[9]  Roy L. Streit,et al.  A comparison of the JPDAF and PMHT tracking algorithms , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[10]  S. Godsill,et al.  Monte Carlo filtering for multi target tracking and data association , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Thia Kirubarajan,et al.  Probabilistic data association techniques for target tracking in clutter , 2004, Proceedings of the IEEE.

[12]  Simon J. Godsill,et al.  Tracking variable number of targets using sequential monte carlo methods , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[13]  P. Pérez,et al.  Tracking multiple objects with particle filtering , 2002 .

[14]  N. Gordon A hybrid bootstrap filter for target tracking in clutter , 1995, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Neil J. Gordon,et al.  Tracking in the presence of intermittent spurious objects and clutter , 1998, Defense, Security, and Sensing.

[16]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[17]  C. Jauffret,et al.  A formulation of multitarget tracking as an incomplete data problem , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Carlo S. Regazzoni,et al.  Real-time video-shot detection for scene surveillance applications , 2000, IEEE Trans. Image Process..

[19]  PeopleIsmail,et al.  W 4 : Who ? When ? Where ? What ? A Real Time System for Detecting and Tracking , 1998 .

[20]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[21]  D. Avitzour Stochastic simulation Bayesian approach to multitarget tracking , 1995 .

[22]  Michael O. Kolawole,et al.  Estimation and tracking , 2002 .