Environment perception is a key enabling technology in autonomous vehicles, and multiple object tracking is an important part of this. High resolution sensors, such as automotive radar and lidar, leads to the so called extended target tracking problem, in which there are multiple detections per tracked object. For computationally feasible multiple extended target tracking, the data association problem must be handled. Previous work has relied on the use of clustering algorithms, together with assignment algorithms, to achieve this. In this paper we present a stochastic optimisation method that directly maximises the desired likelihood function, and solves the problem in a single step, rather than two steps (clustering+assignment). The proposed method is evaluated against previous work in an experiment where Velodyne data is used to track pedestrians, and the results clearly show that the proposed method achieves the best performance, especially in challenging scenarios.


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    Titel :

    Pedestrian tracking using Velodyne data — Stochastic optimization for extended object tracking


    Beteiligte:
    Granstrom, Karl (Autor:in) / Renter, Stephan (Autor:in) / Fatemi, Maryam (Autor:in) / Svensson, Lennart (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    415349 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Pedestrian Tracking Using Veiodyne Data -- Stochastic Optimization for Extended Object Tracking

    Granstrom, Karl / Reuter, Stephan / Fatemi, Maryam et al. | British Library Conference Proceedings | 2017


    Velodyne SLAM

    Moosmann, F. / Stiller, C. | IEEE | 2011


    Multiple Vehicle-like Target Tracking Based on the Velodyne LiDAR

    Zhang, Liang / Li, Qingquan / Li, Ming et al. | Tema Archiv | 2013


    Velodyne SLAM

    Moosmann, F. / Stiller, C. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2011


    PERFORMANCE OF GLOBAL DESCRIPTORS FOR VELODYNE-BASED URBAN OBJECT RECOGNITION

    Chen, T. / Dai, B. / Liu, D. et al. | British Library Conference Proceedings | 2014