Modified particle filter for object tracking in low frame rate video

Object tracking algorithm using modified Particle filter in low frame rate (LFR) video is proposed in this paper, which the object moving significantly and randomly between consecutive frames in the low frame rate situation. Traditionally, Particle filtering use motion transitions to model the movement of the target. However, in object tracking with low frame rate sequences, it is very difficult to model significant random jumps of subjects. The key notion of our solution is that using the object detection and extraction to locate the tracked object, while not using the dynamical function. We propagate the sample set around the detected regions, which the samples are assumed to be uniformly distributed in the neighborhoods of the detected region. It is similar to the general particle filter to propagate samples. Then we compute the likelihood between the target model and the candidate regions, which are based on color histogram distances. Our extensive experiments show that the proposed algorithm performs robustly in a large variety of tracking scenarios.

[1]  Cyril Cauchois,et al.  Robotic assistance: an automatic wheelchair tracking and following functionality by omnidirectional vision , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[3]  Fatih Murat Porikli,et al.  Object tracking in low-frame-rate video , 2005, IS&T/SPIE Electronic Imaging.

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

[5]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[6]  Harry Shum,et al.  Hierarchical Shape Modeling for Automatic Face Localization , 2002, ECCV.

[7]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[8]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[9]  Niclas Bergman,et al.  Recursive Bayesian Estimation : Navigation and Tracking Applications , 1999 .

[10]  Yuan-Kai Wang,et al.  A robust vehicle detection approach , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[11]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

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

[13]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

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

[15]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.