Vehicle target tracking based on surveillance video has important application value for road running state perception and abnormal event detection, and has always been a research hotspot in the field of intelligent transportation. Since the advent of Transfomer, it has achieved superior performance in various tasks of computer vision. The multi-target tracking framework based on Transfomer has also received extensive attention. TransCenter algorithm, as a multi-target tracking network based on Transfomer, has achieved good results in pedestrian tracking tasks. However, in the top-down feature fusion process, the semantic information of high-level features is lost. Combined with the attention mechanism, we propose a multi-scale feature fusion strategy based on attention mechanism, which effectively utilizes the semantic information and detail information in multi-scale features. The effectiveness of the method is verified on the UA-DETRAC dataset. Experiments show that the vehicle tracking effect of this method is better than the benchmark model TransCenter


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    Titel :

    Improved transcenter vehicle tracking algorithm based on attention mechanism feature fusion


    Beteiligte:
    Easa, Said (Herausgeber:in) / Wei, Wei (Herausgeber:in) / Yue, Qiang (Autor:in)

    Kongress:

    Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023) ; 2023 ; Hangzhou, China


    Erschienen in:

    Proc. SPIE ; 12790


    Erscheinungsdatum :

    2023-09-07





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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