The integration of dual-modal features has been pivotal in advancing
RGB-Depth (RGB-D) tracking. Tuttavia, 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.

Questo articolo esplora i giri e le loro implicazioni.

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