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AFTER: Attention-Based Fusion Router for RGBT Tracking
Summary
This study introduces AFTER, an attention-based fusion router for robust Red-Green-Blue-Depth (RGBT) tracking. AFTER dynamically optimizes fusion structures to overcome challenges in dynamic scenarios, improving tracking performance.
Area of Science:
- Computer Vision
- Machine Learning
- Artificial Intelligence
Background:
- Multi-modal feature fusion is crucial for Red-Green-Blue-Depth (RGBT) tracking.
- Existing RGBT trackers often use fixed fusion structures, limiting adaptability to dynamic scenarios.
Purpose of the Study:
- To develop a novel attention-based fusion router (AFTER) for robust RGBT tracking.
- To enable dynamic adaptation of fusion structures to challenging scenarios.
Main Methods:
- Designed a fusion structure space using a hierarchical attention network.
- Each unit in the network represents a fusion operation, and their combinations form fusion structures.
- Developed a dynamic routing algorithm with routers to predict combination weights for efficient fusion structure optimization.
Main Results:
- AFTER demonstrates superior performance compared to state-of-the-art RGBT trackers.
- Extensive experiments on five mainstream RGBT tracking datasets validate the effectiveness of the proposed method.
Conclusions:
- The proposed AFTER method offers a dynamic and adaptive approach to RGBT tracking.
- AFTER's ability to optimize fusion structures enhances robustness in challenging, dynamic environments.
