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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 2130 of 55 papers

TitleStatusHype
Long-term Frame-Event Visual Tracking: Benchmark Dataset and BaselineCode2
Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens0
Temporal Adaptive RGBT Tracking with Modality Prompt0
Bi-directional Adapter for Multi-modal TrackingCode1
Single-Model and Any-Modality for Video Object TrackingCode1
Generative-based Fusion Mechanism for Multi-Modal TrackingCode1
RGB-T Tracking via Multi-Modal Mutual Prompt LearningCode1
Unified Single-Stage Transformer Network for Efficient RGB-T TrackingCode1
EANet: Enhanced Attribute-based RGBT Tracker Network0
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object TrackingCode1
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