In recent years, computer vision technology has developed rapidly and has been applied in various fields. Using this method to detect baby carriage in subway stations can provide technical support for the improvement of future subway station facilities layout. This paper uses the baby carriage detection model to detect the baby carriage that appears in the surveillance video of the subway station. Due to the dense flow of people in subway stations, the frequent flow of people, and the low pixels of the video surveillance system, there are some difficulties in real-time detection of baby carriage. Through the collection and production of the baby carriage data set, the training of the detection model and the final detection verification, we can achieve real-time detection of the baby carriage. The accuracy rate can reach more than 90%.


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    Titel :

    Baby Carriage Detection in Subway Stations Based on YOLOv3


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Zeng, Xiaoqing (Herausgeber:in) / Xie, Xiongyao (Herausgeber:in) / Sun, Jian (Herausgeber:in) / Ma, Limin (Herausgeber:in) / Chen, Yinong (Herausgeber:in) / Zeng, Xiaoqing (Autor:in) / Ai, Siyu (Autor:in) / Huang, Jicheng (Autor:in) / Ma, Zhongzhen (Autor:in) / Min, Gu (Autor:in)

    Kongress:

    International Symposium for Intelligent Transportation and Smart City ; 2022 May 20, 2022 - May 21, 2022



    Erscheinungsdatum :

    28.04.2023


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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