Hardware/software co-design of particle filter and its application in object tracking

This paper presents a hardware/software co-design method for particle filter based on System On Program Chip (SOPC) technique. Considering both the execution speed and design flexibility, we use a NIOS II processor to calculate weight for each particle and a hardware accelerator to update particles. As a result, execution efficiency of the proposed hardware/software co-design method of particle filter is significantly improved while maintaining design flexibility for various applications. To demonstrate the performance of the proposed approach, a real-time object tracking system is established and presented in this paper. Experimental results have demonstrated the proposed method have satisfactory results in real-time tracking of objects in video sequences.

[1]  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.

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

[3]  Ching-Chang Wong,et al.  Hardware/software co-design for particle swarm optimization algorithm , 2010, SMC.

[4]  Jae Wook Jeon,et al.  Multiple Objects Tracking Circuit using Particle Filters with Multiple Features , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[5]  Jae Wook Jeon,et al.  A Real-Time Object Tracking System Using a Particle Filter , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  P. Djurić,et al.  Particle filtering , 2003, IEEE Signal Process. Mag..

[7]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[8]  N. D. Freitas Rao-Blackwellised particle filtering for fault diagnosis , 2002 .

[9]  Pingyuan Cui,et al.  Adaptive MCMC Particle Filter for Nonlinear and Non-Gaussian State Estimation , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

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

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

[12]  G. Kitagawa Smoothness priors analysis of time series , 1996 .

[13]  Zongli Lin,et al.  Fuzzy Particle Filter Used for Tracking of Leukocytes , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.

[14]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[15]  Petar M. Djuric,et al.  Gaussian particle filtering , 2003, IEEE Trans. Signal Process..

[16]  Jae Wook Jeon,et al.  Combine Kalman filter and particle filter to improve color tracking algorithm , 2007, 2007 International Conference on Control, Automation and Systems.

[17]  George Marsaglia,et al.  The Monty Python method for generating random variables , 1998, TOMS.

[18]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[19]  ByoungChul Ko,et al.  Object Tracking Using Particle Filters in Moving Camera , 2012 .

[20]  Pramod K. Varshney,et al.  Curvature nonlinearity measure and filter divergence detector for nonlinear tracking problems , 2008, 2008 11th International Conference on Information Fusion.

[21]  Mónica F. Bugallo,et al.  Multiple Particle Filtering , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[22]  Jae Wook Jeon,et al.  Object Tracking Circuit using Particle Filter with Multiple Features , 2006, 2006 SICE-ICASE International Joint Conference.

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