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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 1120 of 55 papers

TitleStatusHype
Bi-directional Adapter for Multi-modal TrackingCode1
Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and MethodCode1
Breaking Modality Gap in RGBT Tracking: Coupled Knowledge DistillationCode1
Single-Model and Any-Modality for Video Object TrackingCode1
Bridging Search Region Interaction With Template for RGB-T TrackingCode1
Generative-based Fusion Mechanism for Multi-Modal TrackingCode1
AFter: Attention-based Fusion Router for RGBT TrackingCode1
LasHeR: A Large-scale High-diversity Benchmark for RGBT TrackingCode1
Attribute-Based Progressive Fusion Network for RGBT TrackingCode1
MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T TrackingCode1
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