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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 1–25 of 55 papers

TitleStatusHype
Lightweight RGB-T Tracking with Mobile Vision Transformers—0
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T TrackingCode0
Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking—0
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
SUTrack: Towards Simple and Unified Single Object TrackingCode2
Exploiting Multimodal Spatial-temporal Patterns for Video Object TrackingCode2
Breaking Modality Gap in RGBT Tracking: Coupled Knowledge DistillationCode1
Cross Fusion RGB-T Tracking with Bi-directional Adapter—0
RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba—0
MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T TrackingCode1
Cross-modulated Attention Transformer for RGBT Tracking—0
Transformer-based RGB-T Tracking with Channel and Spatial Feature FusionCode1
AFter: Attention-based Fusion Router for RGBT TrackingCode1
Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and MethodCode1
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
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object TrackingCode2
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning—0
Long-term Frame-Event Visual Tracking: Benchmark Dataset and BaselineCode2
Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens—0
Temporal Adaptive RGBT Tracking with Modality Prompt—0
Bi-directional Adapter for Multi-modal TrackingCode1
Single-Model and Any-Modality for Video Object TrackingCode1
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