RGB-D Tracking via Hierarchical Modality Aggregation and Distribution Network

The integration of dual-modal features has been pivotal in advancing RGB-Depth (RGB-D) tracking. However, current trackers are less efficient and focus solely on single-level features, resulting in weaker robustness in fusion and slower speeds that fail to meet the demands of real-world applications. In this paper, we introduce a novel network, denoted as HMAD (Hierarchical Modality Aggregation and Distribution), which addresses these challenges. HMAD leverages the distinct feature representation strengths of RGB and depth modalities, giving prominence to a hierarchical approach for feature distribution and fusion, thereby enhancing the robustness of RGB-D tracking. Experimental results on various RGB-D datasets demonstrate that HMAD achieves state-of-the-art performance. Moreover, real-world experiments further validate HMAD's capacity to effectively handle a spectrum of tracking challenges in real-time scenarios.

@article{xu2025_2504.17595,
  title={ RGB-D Tracking via Hierarchical Modality Aggregation and Distribution Network },
  author={ Boyue Xu and Yi Xu and Ruichao Hou and Jia Bei and Tongwei Ren and Gangshan Wu },
  journal={arXiv preprint arXiv:2504.17595},
  year={ 2025 }
}