Vehicle attribute recognition in urban traffic monitoring is the core task in urban intelligent transportation system, Vehicle attribute recognition mainly includes sub-tasks such as vehicle type identification, vehicle color recognition, and vehicle brand recognition. Most of the current solutions are single-task learning based on a single attribute, and there are few studies on complex learning tasks with multiple attributes. This paper constructs a vehicle multi-attribute data set for the multi-attribute characteristics of vehicles, and based on the training mode of multitasking learning, Separate vehicle brand recognition network and vehicle color recognition network that are more suitable for their respective characteristics, and integrate vehicle multi-attribute identification network into the same model structure for training. By comparing the current popular neural network with several attributes, the final experiment shows that the vehicle multi-attribute recognition model trained by the algorithm can obtain better recognition results and higher accuracy.


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    Titel :

    Vehicle Attribute Recognition Algorithm Based on Multi-task Learning


    Beteiligte:
    Sun, Jingying (Autor:in) / Jia, Chengzhe (Autor:in) / Shi, Zhiguo (Autor:in)


    Erscheinungsdatum :

    01.08.2019


    Format / Umfang :

    484559 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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