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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
Cross-modulated Attention Transformer for RGBT Tracking0
Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking0
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation0
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning0
Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens0
Temporal Adaptive RGBT Tracking with Modality Prompt0
EANet: Enhanced Attribute-based RGBT Tracker Network0
RGB-T Tracking Based on Mixed Attention0
Self-Supervised RGB-T Tracking with Cross-Input Consistency0
Efficient RGB-T Tracking via Cross-Modality Distillation0
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