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Visual Tracking Using Monte Carlo Sampling and Background Subtraction  

Kim, Hyun-Cheol (Dept. of Image Engineering. Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Paik, Joon-Ki (Dept. of Image Engineering. Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.48, no.5, 2011 , pp. 16-22 More about this Journal
Abstract
This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.
Keywords
multi-object tracking; particle filter; monte carlo sampling;
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