Title :
An improved MCMC particle filter based on greedy algorithm for video object tracking
Author :
Wang, Song ; Wang, Huiyuan ; Wang, Xiufen
Author_Institution :
Sch. of Inf. Sci. & Eng., Shandong Univ., Jinan, China
Abstract :
In this paper, an improved MCMC (Markov Chain Monte Carlo) particle filter for video tracking is proposed. MCMC plays an important role in video tracking and so is of popular use in this field. However, it is still very difficult to satisfy the requirement of real-time application for its high computation complexity. To solve this problem, the concept of greedy algorithm is adopted questioning this study. Experiment results show that the proposed approach performs well in both tracking robustness and computational efficiency.
Keywords :
Markov processes; Monte Carlo methods; computational complexity; greedy algorithms; particle filtering (numerical methods); target tracking; video signal processing; computation complexity; computational efficiency; greedy algorithm; improved Markov Chain Monte Carlo particle filter; realtime application; tracking robustness; video object tracking; Approximation algorithms; Greedy algorithms; Kalman filters; Markov processes; Particle filters; Target tracking;
Conference_Titel :
Communication Technology (ICCT), 2011 IEEE 13th International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-1-61284-306-3
DOI :
10.1109/ICCT.2011.6158000
