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<p>RGB-T crowd counting is a challenging task that integrates RGB and thermal images to address the limitations of RGB-only approaches in scenes with poor illumination or occlusion. While transformer-based models have shown remarkable success in terms of capturing long-range dependencies, their high computational demands limit their practical applicability. To address this issue, a novel hybrid model named TransMambaCC, which fuses the analytical strength of transformer with the computational efficiency of Mamba, is proposed. This integration not only improves crowd analysis performance, but also significantly reduces computational overhead of the model. Additionally, a Pyramid Mamba module is innovatively designed to address the head-scale variations observed in congested scenes. Extensive experiments conducted on the RGBT-CC dataset demonstrate the superiority of TransMambaCC over the existing approaches in terms of both accuracy and efficiency. Furthermore, the model exhibits strong generalization capabilities, as evidenced by its performance on the ShanghaiTechRGBD dataset. The code is available at <a href="https://github.com/yjchen3250/TransMambaCC">https://github.com/yjchen3250/TransMambaCC</a>.</p>

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TransMambaCC: Integrating Transformer and Pyramid Mamba Network for RGB-T Crowd Counting

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Yangjian Chen,
  • Huailin Zhao,
  • Liangjun Huang,
  • Yubo Yang,
  • Wencan Kang,
  • Jianwei Zhang

摘要

RGB-T crowd counting is a challenging task that integrates RGB and thermal images to address the limitations of RGB-only approaches in scenes with poor illumination or occlusion. While transformer-based models have shown remarkable success in terms of capturing long-range dependencies, their high computational demands limit their practical applicability. To address this issue, a novel hybrid model named TransMambaCC, which fuses the analytical strength of transformer with the computational efficiency of Mamba, is proposed. This integration not only improves crowd analysis performance, but also significantly reduces computational overhead of the model. Additionally, a Pyramid Mamba module is innovatively designed to address the head-scale variations observed in congested scenes. Extensive experiments conducted on the RGBT-CC dataset demonstrate the superiority of TransMambaCC over the existing approaches in terms of both accuracy and efficiency. Furthermore, the model exhibits strong generalization capabilities, as evidenced by its performance on the ShanghaiTechRGBD dataset. The code is available at https://github.com/yjchen3250/TransMambaCC.