Multiple object tracking is a vital task for autonomous vehicle environment perception. In this paper, we design a novel multi-object tracking method for autonomous vehicles. In the detection section, we utilize popular Faster-RCNN as our baseline method. Then, in data association, we combine appearance, motion, and interaction model to build a unified feature descriptor to explore the nature of tracking object. We evaluate our algorithm on a popular and standard benchmark and compare with the state-of-the-art methods. The results denote that our algorithm achieve good performance at high frame rates.


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    Titel :

    A Novel Multiple Object Tracking Algorithm for Autonomous Vehicles


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wuhong (Herausgeber:in) / Baumann, Martin (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Deng, Hai (Autor:in) / Gao, Ming (Autor:in) / Jin, Li-sheng (Autor:in) / Guo, Bai-cang (Autor:in)


    Erscheinungsdatum :

    24.03.2020


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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