Vehicle classification and tracking is considered as one of the most challenging problems in the field of pattern recognition. In this paper, Particle Swarm Optimization (PSO) based method is exploited to recognize vehicle classes. Vehicle features, such as vehicle size, shape information, contour information are extracted. Each vehicle class is encoded as a centroid with multidimensional feature and PSO is employed to search the optimal position for each class centroid based on fitness function. After vehicle classification, an improved meanshift algorithm is presented for vehicle tracking. The algorithm’s evaluations on video image series, moving vehicle detection, vehicle classification and tracking are respectively conducted. The results show that PSO ensures a promising and stable performances in recognizing these vehicle classes, and the improved meanshift algorithm can achieve accuracy and real-time for tracking moving vehicles.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Classification and Tracking Based on Particle Swarm Optimization and Meanshift



    Published in:

    Advanced Materials Research ; 121-122 ; 417-422


    Publication date :

    2010-06-30


    Size :

    6 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Multiple Vehicle Tracking Using Meanshift Algorithm and 8-point Connectivity

    Nissimagoudar, Prabha / Algur, Nihal / Bonageri, Nikhil et al. | Springer Verlag | 2023


    Online Updating Appearance Generative Mixture Model for Meanshift Tracking

    Tu, J. / Tao, H. / Huang, T. | British Library Conference Proceedings | 2006



    Tracking of Traffic Monitoring Targets in Complicated Traffic Scene Based on MeanShift Algorithm

    Zhao, J. Y. / Cui, J. | British Library Conference Proceedings | 2015