We propose a method for estimating the pose of a bicycle rider from images by integrating the relationship between bicycle parts and the human body and modeling them as a single skeleton for traffic analysis. The feature of this method is that the bicycle rider and the bicycle are represented as an integrated skeletal model instead of separating the skeletal representation of the bicycle parts and the human body. We call this the Bicycle Rider Model. To train the Bicycle Rider Model, we handcrafted our video dataset. In the dataset, we annotate the center of the handlebars, the crankshaft, and the front end of the front wheel as the new keypoints of the skeleton. We confirmed that the Bicycle Rider Model can estimate the pose of the bicycle rider.


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    Titel :

    Bicycle rider pose estimation using a human skeleton model with bicycle parts


    Beteiligte:
    Nakajima, Masayuki (Herausgeber:in) / Chang, Chuan-Yu (Herausgeber:in) / Yeh, Chia-Hung (Herausgeber:in) / Kim, Jae-Gon (Herausgeber:in) / Qian, Kemao (Herausgeber:in) / Lau, Phooi Yee (Herausgeber:in) / Shiromo, Sora (Autor:in) / Kameda, Yoshinari (Autor:in)

    Kongress:

    International Workshop on Advanced Imaging Technology (IWAIT) 2025 ; 2025 ; Douliu City, Taiwan


    Erschienen in:

    Proc. SPIE ; 13510


    Erscheinungsdatum :

    05.02.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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