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<p>This study proposes a novel Multi Curvelet Transformer Network (MCTN) for fine-grained human behavior recognition in dynamic video scenarios. A key challenge in this field lies in accurately identifying human actions under adverse conditions such as motion blur, occlusion, and varying illumination. To address this, we introduce a motion blur restoration module leveraging the curvelet transform to enhance motion image clarity, thereby improving downstream behavior detection. Furthermore, we enhance the Transformer architecture by embedding curvelet-based multi-scale attention mechanisms, which significantly improve the model’s ability to extract spatial-temporal features at different resolutions. The proposed network also adopts a multi-curvelet transform structure to deepen semantic representation. Experimental results on benchmark datasets, including an action recognition dataset and the MSCOCO dataset, demonstrate that MCTN achieves superior performance, reaching a mean average precision (mAP) of 0.822. These results underscore the potential of MCTN in real-time intelligent video analysis and human-computer interaction applications.</p>

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Curvelet-enhanced transformer architecture for blurred action fine-grained detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月31日

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  • Yuxiang Ren,
  • Zhetao Guo,
  • Wei Zhang,
  • Yushi Shen,
  • Ying Xing

摘要

This study proposes a novel Multi Curvelet Transformer Network (MCTN) for fine-grained human behavior recognition in dynamic video scenarios. A key challenge in this field lies in accurately identifying human actions under adverse conditions such as motion blur, occlusion, and varying illumination. To address this, we introduce a motion blur restoration module leveraging the curvelet transform to enhance motion image clarity, thereby improving downstream behavior detection. Furthermore, we enhance the Transformer architecture by embedding curvelet-based multi-scale attention mechanisms, which significantly improve the model’s ability to extract spatial-temporal features at different resolutions. The proposed network also adopts a multi-curvelet transform structure to deepen semantic representation. Experimental results on benchmark datasets, including an action recognition dataset and the MSCOCO dataset, demonstrate that MCTN achieves superior performance, reaching a mean average precision (mAP) of 0.822. These results underscore the potential of MCTN in real-time intelligent video analysis and human-computer interaction applications.