In this paper, we propose a co-learning particle filter approach for vehicle tracking, which is very important for intelligent vehicle. The proposal distribution of the particle filter is a combination of an extra support vector machine (SVM) detector and the motion prior. Previous works focusing on how to online update the detector or the observation likelihood using the tracking results. These approaches belong to "self-learning" fashion and easily tend to drift. The major difference between the proposed approach and previous works is that the SVM detector and the likelihood function can be mutually updated in a co-learning manner. By adopting the co-learning technology, the unlabelled samples which are generated during tracking are utilized to progressively modify the SVM detector and update the observation likelihood; therefore the resulting tracker is more robust and effectively avoids the drift problem. Finally, the performance of the proposed approach is evaluated using extensive real visual tracking examples.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vehicle tracking based on co-learning particle filter


    Beteiligte:
    Ye, Weilong (Autor:in) / Liu, Huaping (Autor:in) / Sun, Fuchun (Autor:in) / Gao, Meng (Autor:in)


    Erscheinungsdatum :

    2009


    Format / Umfang :

    6 Seiten, 26 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    On-Road Vehicle Tracking Using Part-Based Particle Filter

    Fang, Yongkun / Wang, Chao / Yao, Wen et al. | IEEE | 2019


    Variable-mass particle filter for road-constrained vehicle tracking

    Kravaritis, Giorgos / Mulgrew, Bernard | Tema Archiv | 2008


    On-road multi-vehicle tracking algorithm based on an improved particle filter

    Liu, Peixun / Li, Wenhui / Wang, Ying et al. | IET | 2015

    Freier Zugriff

    Particle filter based joint tracking and classification

    Zhan, Kun / Xu, Long / Jiang, Hong et al. | IEEE | 2014


    On‐road multi‐vehicle tracking algorithm based on an improved particle filter

    Liu, Peixun / Li, Wenhui / Wang, Ying et al. | Wiley | 2015

    Freier Zugriff