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Rgb-T Tracking

RGBT tracking, or RGB-Thermal tracking, is a sophisticated method utilized in computer vision for tracking objects across both RGB and thermal infrared modalities. This technique combines information from both RGB and thermal imagery to enhance object detection and tracking performance, particularly in challenging environments where lighting conditions may vary or be limited. By integrating data from these two modalities, RGBT tracking systems can effectively compensate for the limitations of each individual modality, such as the inability of RGB cameras to capture clear images in low-light or adverse weather conditions, and the inability of thermal cameras to accurately identify object details. RGBT tracking algorithms typically involve sophisticated fusion techniques to combine information from RGB and thermal sensors, enabling robust and accurate object tracking in diverse scenarios ranging from surveillance and security applications to autonomous vehicles and search and rescue operations.

Papers

Showing 4150 of 55 papers

TitleStatusHype
Prompting for Multi-Modal Tracking0
0/1 Deep Neural Networks via Block Coordinate Descent0
Dynamic Fusion Network for RGBT Tracking0
Siamese Infrared and Visible Light Fusion Network for RGB-T Tracking0
Duality-Gated Mutual Condition Network for RGBT Tracking0
RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss0
MIRNet: Learning multiple identities representations in overlapped speech0
Challenge-Aware RGBT Tracking0
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking0
Cross-Modal Pattern-Propagation for RGB-T Tracking0
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