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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    Title :

    A Novel Multiple Object Tracking Algorithm for Autonomous Vehicles


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Wuhong (editor) / Baumann, Martin (editor) / Jiang, Xiaobei (editor) / Deng, Hai (author) / Gao, Ming (author) / Jin, Li-sheng (author) / Guo, Bai-cang (author)


    Publication date :

    2020-03-24


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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