Smart subway is an important part of the comprehensive, green, safe and intelligent three-dimensional modern urban transportation system, Among them, The Urban Rail Intelligent Security has become an important guarantee for subway safety. Aiming at the problems of scattered security system construction, delayed data transmission and low intellectualization, this paper explores a kind of urban rail security system architecture under the cooperation of computing center and edge equipment. The video surveillance system is taken as a typical research object to carry out the research on cloud edge collaboration technology. Based on resource virtualization technology, this architecture uses multi-source data fusion and double-stream convolution algorithm to identify abnormal behaviors, and an effective cloud edge resource allocation mechanism is established by combining two-way auction algorithm. Furthermore, a video surveillance system based on cloud edge collaboration is built, and the relevant functions are tested experimentally. The results show that the video surveillance system based on cloud edge collaboration theory can intelligently identify various application scenarios of rail transit, reduce the pressure of station security and improve the level of station operation and management.


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    Title :

    Research on Key Technologies of Cloud-Side Collaboration for Urban Rail Intelligent Security


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Liang, Jianying (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Zhou, Chao (author) / Du, Chengxin (author) / Wang, Zhifei (author) / Meng, Yukun (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Publication date :

    2022-02-23


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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