Exploiting All Mamba Fusion for Efficient RGB-D Tracking

Despite the progress made through deep learning, existing Visual Object Tracking (VOT) frameworks struggle with real-world challenges. Recent approaches incorporate additional modalities like Depth, Thermal Infrared, and Language to enhance the robustness of VOT, particularly with the improvement of the depth sensor precision, facilitating RGB-D tracking. However, current RGB-D trackers often copy RGB tracking paradigms, leading to inefficiency due to two-stream architectures that fail to exploit heterogeneous features, and reliance on simplistic or large-parameter fusion methods. To address these challenges, we propose AMTrack, a one-stream RGB-D tracker leveraging Mamba's linear complexity for simultaneous feature extraction and two-stage cross-modal feature fusion. Our innovation also includes a low-parameter Multimodal Mix Mamba (3M) module, which optimizes deep feature fusion and reduces computational overhead. The advantage of the 3M module stems from our Multimodal State Space Model (MSSM), a multimodal feature interaction component reconstructed based on SSM. Experiments across multiple RGB-D tracking datasets indicate that AMTrack achieves superior performance with lower parameters and memory demands compared to state-of-the-arts.

Authors

• Ge Ying Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University School of Computer Science and Technology, Zhejiang Normal University
• Dawei Zhang
• Chengzhuan Yang School of Computer Science and Technology, Zhejiang Normal University
• Wei Liu University of Technology Sydney
• Sang-Woon Jeon School of Computer Science and Technology, Zhejiang Normal University
• Hua Wang
• Changqin Huang Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, China
• Zhonglong Zheng

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