Although pedestrian detection has achieved promising performance with the development of deep learning techniques, it remains a great challenge to detect heavily occluded pedestrians in crowd scenes. Therefore, to make the anchor-free network pay more attention to learning the hard examples of occluded pedestrians, we propose a simple but effective Occlusion-aware Anchor-Free Network (namely OAF-Net) for pedestrian detection in crowd scenes. Specifically, we first design a novel occlusion-aware detection head, which includes three separate center prediction branches combining with the scale and offset prediction branches. In the detection head of OAF-Net, occluded pedestrian instances are assigned to the most suitable center prediction branch according to the occlusion level of human body. To optimize the center prediction, we accordingly propose a novel weighted Focal Loss where pedestrian instances are assigned with different weights according to their visibility ratios, so that the occluded pedestrians are up-weighted during the training process. Our OAF-Net is able to model different occlusion levels of pedestrian instances effectively, and can be optimized towards catching a high-level understanding of the hard training samples of occluded pedestrians. Experiments on the challenging CityPersons, Caltech, and CrowdHuman benchmarks sufficiently validate the efficacy of our OAF-Net for pedestrian detection in crowd scenes.


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    Titel :

    OAF-Net: An Occlusion-Aware Anchor-Free Network for Pedestrian Detection in a Crowd


    Beteiligte:
    Li, Qiming (Autor:in) / Su, Yijing (Autor:in) / Gao, Yin (Autor:in) / Xie, Feng (Autor:in) / Li, Jun (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2022


    Format / Umfang :

    3479355 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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