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In crowded scenes, it is challenging to extract the appearance features of pedestrians due to occlusion issues. To enhance the semantic features of pedestrians, we propose the Pedestrian Attribute Distillation Fusion Model (PADFM). In the absence of attribute labels in pedestrian detection datasets, PADFM acquires the capability to predict pedestrian attributes through knowledge distillation. It integrates pedestrian attribute information into the detection process, thereby increasing the basis for pedestrian discrimination and improving the detection performance of pedestrians in occluded scenes. Furthermore, since the PADFM can simultaneously output detection results and their attribute information, it provides richer information for related applications in the field of pedestrian detection.

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Pedestrian Attribute Distillation Fusion Model

  • 会议论文

  • 发表日期：

    2025年

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  • Huilin Ding,
  • Yuzhou Gong,
  • Shoudong Han,
  • Hao Ding,
  • Helong Shang,
  • Hong Wang,
  • Jian Liu

摘要

In crowded scenes, it is challenging to extract the appearance features of pedestrians due to occlusion issues. To enhance the semantic features of pedestrians, we propose the Pedestrian Attribute Distillation Fusion Model (PADFM). In the absence of attribute labels in pedestrian detection datasets, PADFM acquires the capability to predict pedestrian attributes through knowledge distillation. It integrates pedestrian attribute information into the detection process, thereby increasing the basis for pedestrian discrimination and improving the detection performance of pedestrians in occluded scenes. Furthermore, since the PADFM can simultaneously output detection results and their attribute information, it provides richer information for related applications in the field of pedestrian detection.