The matching between 3D model projection and 2D image data is a key technique for model based localization, recognition and tracking problems. Firstly, we propose a fitness function to evaluate the matching degree that uses image gradient information in the neighborhood of model projection. The weighting adjustment and the normalization for visible model projection are involved, which improves the correctness and robustness of fitness function. The fitness function is used for vehicle localization and the 3D pose is reduced to location and orientation. Then, we present a direct search optimization method with 3×3 search kernel for location estimation. The “disturbed particles” is used to avoid falling into local optimum and the coarse-to-fine optimization strategy is adopted to greatly reduce computational cost. Finally, we propose a 3D pose estimator to find location and orientation by optimizing the fitness function within orientation range. Experiments on real traffic surveillance videos reveal that the proposed optimization algorithm is effective and both fitness function and 3D pose estimator are correct and robust against clutter and occlusion.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Model based vehicle localization for urban traffic surveillance using image gradient based matching


    Beteiligte:
    Zheng, Yuan (Autor:in) / Peng, Silong (Autor:in)


    Erscheinungsdatum :

    01.09.2012


    Format / Umfang :

    615281 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Image Processing based Traffic Surveillance

    Chandrakanth, D K / Prabhu, Siddharth / Preetham, G M et al. | IEEE | 2024


    Fast Automatic Vehicle Annotation for Urban Traffic Surveillance

    Zhou, Yi / Liu, Li / Shao, Ling et al. | IEEE | 2018


    Airborne moving vehicle detection for urban traffic surveillance

    Lin, Renjun / Cao, Xianbin / Xu, Yanwu et al. | IEEE | 2008