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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
Adaptive Perception for Unified Visual Multi-modal Object Tracking—0
BTMTrack: Robust RGB-T Tracking via Dual-template Bridging and Temporal-Modal Candidate Elimination—0
PURA: Parameter Update-Recovery Test-Time Adaption for RGB-T Tracking—0
Cross Fusion RGB-T Tracking with Bi-directional Adapter—0
RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba—0
Cross-modulated Attention Transformer for RGBT Tracking—0
Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking—0
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation—0
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning—0
Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens—0
Temporal Adaptive RGBT Tracking with Modality Prompt—0
EANet: Enhanced Attribute-based RGBT Tracker Network—0
RGB-T Tracking Based on Mixed Attention—0
Self-Supervised RGB-T Tracking with Cross-Input Consistency—0
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
Dynamic Fusion Network for RGBT Tracking—0
Siamese Infrared and Visible Light Fusion Network for RGB-T Tracking—0
Duality-Gated Mutual Condition Network for RGBT Tracking—0
RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss—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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