On-road visual vehicle tracking using Markov chain Monte Carlo particle filtering with metropolis sampling
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- 作者：J. Arr贸spide (1) jal@gti.ssr.upm.es
L. Salgado (1) - 关键词：Key Words Intelligent vehicles – ; Image analysis – ; Object tracking – ; Monte Carlo methods
- 刊名：International Journal of Automotive Technology
- 出版年：2012
- 出版时间：October 2012
- 年：2012
- 卷：13
- 期：6
- 页码：955-961
- 全文大小：1.0 MB
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- ISSN：1976-3832
文摘
In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.