ON-ROAD VISUAL VEHICLE TRACKING USING MARKOV CHAIN MONTE CARLO PARTICLE FILTERING WITH METROPOLIS SAMPLING

被引:9
作者
Arrospide, J. [1 ]
Salgado, L. [1 ]
机构
[1] Univ Politecn Madrid, Grp Tratamiento Imagenes, ETSI Telecomunicac, E-28040 Madrid, Spain
关键词
Intelligent vehicles; Image analysis; Object tracking; Monte Carlo methods; MULTIPLE; HUMANS; SYSTEM;
D O I
10.1007/s12239-012-0097-1
中图分类号
TH [机械、仪表工业];
学科分类号
120111 [工业工程];
摘要
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.
引用
收藏
页码:955 / 961
页数:7
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