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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 3140 of 55 papers

TitleStatusHype
MIRNet: Learning multiple identities representations in overlapped speech0
FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking0
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation0
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking0
0/1 Deep Neural Networks via Block Coordinate Descent0
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning0
Prompting for Multi-Modal Tracking0
PURA: Parameter Update-Recovery Test-Time Adaption for RGB-T Tracking0
Adaptive Perception for Unified Visual Multi-modal Object Tracking0
RGB-T Object Tracking:Benchmark and Baseline0
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