A Particle Filtering and DSmT Based Approach for Conflict Resolving in case of Target Tracking with Multiple Cues

In this paper, we propose an efficient and robust method for multiple targets tracking in cluttered scenes using multiple cues. Our approach combines the use of Monte Carlo sequential filtering for tracking and Dezert-Smarandache theory (DSmT) to integrate the information provided by the different cues. The use of DSmT provides the necessary framework to quantify and overcome the conflict that might appear between the cues due to the occlusion. Our tracking approach is tested with color and location cues on a cluttered scene where multiple targets are involved in partial or total occlusion.

[1]  Denis Pellerin,et al.  Forward-Backward-Viterbi Procedures in the Transferable Belief Model for State Sequence Analysis Using Belief Functions , 2007, ECSQARU.

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

[3]  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).

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

[5]  Juan I. Nieto,et al.  Multiple target tracking using Sequential Monte Carlo Methods and statistical data association , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[6]  Kamal Premaratne,et al.  Evidence Filtering , 2007, IEEE Transactions on Signal Processing.

[7]  Branko Ristic,et al.  A particle filter for joint detection and tracking of color objects , 2007, Image Vis. Comput..

[8]  Rajeev Sharma,et al.  Adaptive texture and color segmentation for tracking moving objects , 2002, Pattern Recognit..

[9]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[10]  Brendan McCane,et al.  Algorithmic Fusion for More Robust Feature Tracking , 2002, International Journal of Computer Vision.

[11]  Dinggang Shen,et al.  Lane detection and tracking using B-Snake , 2004, Image Vis. Comput..

[12]  D. Pellerin,et al.  Human action recognition in videos based on the Transferable Belief Model Application to athletics jumps , 2007 .

[13]  Jean Dezert,et al.  Applications and Advances of DSmT for Information Fusion , 2004 .

[14]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[15]  Xueyin Lin,et al.  Moving object tracking under varying illumination conditions , 2006, Pattern Recognit. Lett..