Dynamic Proposal Variance and Optimal Particle Allocation in Particle Filtering for Video Tracking

This paper presents a novel particle allocation approach to particle filtering which minimizes the total tracking distortion for a fixed number of particles over a video sequence. We define the tracking distortion as the variance of the error between the true state and estimated state and use rate-distortion theory to determine the optimal particle number and memory size allocation under fixed particle number and memory constraints, respectively. We subsequently provide an algorithm for simultaneous adjustment of the proposal variance and particle number for optimal particle allocation in video tracking systems. Experimental results are used to evaluate the proposed video tracking system and demonstrate its utility for target tracking in numerical examples and video sequences. We demonstrate the superiority of the proposed dynamic proposal variance and optimal particle allocation algorithm in comparison to traditional particle allocation methods, i.e., a fixed number of particles per frame.

[1]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[2]  Dieter Fox,et al.  Adapting the Sample Size in Particle Filters Through KLD-Sampling , 2003, Int. J. Robotics Res..

[3]  Arnaud Doucet,et al.  A survey of convergence results on particle filtering methods for practitioners , 2002, IEEE Trans. Signal Process..

[4]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.

[5]  Michael Isard,et al.  Learning and Classification of Complex Dynamics , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  William Fitzgerald,et al.  A Bayesian approach to tracking multiple targets using sensor arrays and particle filters , 2002, IEEE Trans. Signal Process..

[7]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[8]  Yao Wang,et al.  Video Processing and Communications , 2001 .

[9]  Toby Berger,et al.  Rate distortion theory : a mathematical basis for data compression , 1971 .

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

[11]  Kai-Kuang Ma,et al.  Adaptive irregular pattern search with zero-motion prejudgement for fast block-matching motion estimation , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

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

[13]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[14]  Dan Schonfeld,et al.  A complete system for head tracking using motion-based particle filter and randomly perturbed active contour , 2005, IS&T/SPIE Electronic Imaging.

[15]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[16]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.

[17]  Jerry D. Gibson,et al.  Digital coding of waveforms: Principles and applications to speech and video , 1985, Proceedings of the IEEE.

[18]  Daphne Koller,et al.  Using Learning for Approximation in Stochastic Processes , 1998, ICML.

[19]  Luc Van Gool,et al.  Object Tracking with an Adaptive Color-Based Particle Filter , 2002, DAGM-Symposium.

[20]  Dan Schonfeld,et al.  Automatic multi-head detection and tracking system using a novel detection-based particle filter and data fusion , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[21]  Dan Schonfeld,et al.  Fast object tracking using adaptive block matching , 2005, IEEE Transactions on Multimedia.

[22]  Dan Schonfeld,et al.  Real-Time Distributed Multi-Object Tracking Using Multiple Interactive Trackers and a Magnetic-Inertia Potential Model , 2007, IEEE Transactions on Multimedia.

[23]  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.

[24]  Alvaro Soto,et al.  Self Adaptive Particle Filter , 2005, IJCAI.

[25]  Simon J. Godsill,et al.  Improvement Strategies for Monte Carlo Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.