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.


    Access

    Download


    Export, share and cite



    Title :

    On-road visual vehicle tracking using Markov chain Monte Carlo with metropolis sampling



    Publication date :

    2012-10-01


    Remarks:

    International Journal of Automotive Technology, ISSN 1229-9138, 2012-10, Vol. 13, No. 6


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629







    Markov Chain Monte Carlo Modular Ensemble Tracking

    Penne, T. / Tilmant, C. / Chateau, T. et al. | British Library Online Contents | 2013


    Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference

    Krpelik, Daniel / Aslett, Louis J. M. / Coolen, Frank P. A. | Springer Verlag | 2021