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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 26–50 of 55 papers

TitleStatusHype
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 Network—0
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object TrackingCode1
RGB-T Tracking Based on Mixed Attention—0
Visual Prompt Multi-Modal TrackingCode2
Self-Supervised RGB-T Tracking with Cross-Input Consistency—0
Bridging Search Region Interaction With Template for RGB-T TrackingCode1
Efficient RGB-T Tracking via Cross-Modality Distillation—0
Prompting for Multi-Modal Tracking—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New BaselineCode1
Attribute-Based Progressive Fusion Network for RGBT TrackingCode1
Dynamic Fusion Network for RGBT Tracking—0
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T TrackingCode1
LasHeR: A Large-scale High-diversity Benchmark for RGBT TrackingCode1
Siamese Infrared and Visible Light Fusion Network for RGB-T Tracking—0
Multi-modal Visual Tracking: Review and Experimental ComparisonCode1
RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss—0
Duality-Gated Mutual Condition Network for RGBT Tracking—0
MIRNet: Learning multiple identities representations in overlapped speech—0
Challenge-Aware RGBT Tracking—0
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking—0
Cross-Modal Pattern-Propagation for RGB-T Tracking—0
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