Using vehicles and roads to perceive pedestrians cooperatively can eliminate the visual limitation of a single vehicle, thus improve the pedestrian collision warning capability of the driving assistance system. In this paper, we propose a vehicle-road pedestrian re-identification (ReID) system for vehicle-road cooperative perception of pedestrians, which can provide real-time and reliable ReID results under the premise of minimizing the occupation of communication resources. Among the system, we propose the CNN-LSH feature extraction algorithm and adaptive feature dimension reduction algorithm to adjust the dimension of pedestrian images features effectively according to the status of wireless channel, and use Hamming distance metric to simplify the calculation of feature similarity, which accelerates the speed of image feature matching in ReID. Experimental results demonstrate that the vehicle-road ReID system can significantly reduce the communication overhead and computation time while providing reliable ReID results in real-time.


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    Titel :

    Pedestrian Re-identification with Adaptive Feature Dimension Reduction for Vehicle-Road Cooperative Perception


    Beteiligte:
    Zeng, Yixin (Autor:in) / Sun, Chenyang (Autor:in) / Ding, Liqin (Autor:in) / Hu, Jianyao (Autor:in) / Chen, Zhenwu (Autor:in) / Tian, Yu (Autor:in) / Wang, Yang (Autor:in)


    Erscheinungsdatum :

    20.09.2020


    Format / Umfang :

    306938 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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