Rgb-T Tracking
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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.
논문
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Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking
Adaptive Perception for Unified Visual Multi-modal Object Tracking
BTMTrack: Robust RGB-T Tracking via Dual-template Bridging and Temporal-Modal Candidate Elimination
SUTrack: Towards Simple and Unified Single Object Tracking
Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking
Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation
Cross Fusion RGB-T Tracking with Bi-directional Adapter
RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba
MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking
Cross-modulated Attention Transformer for RGBT Tracking
Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion
AFter: Attention-based Fusion Router for RGBT Tracking
Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Method
Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens
Temporal Adaptive RGBT Tracking with Modality Prompt
Bi-directional Adapter for Multi-modal Tracking
Single-Model and Any-Modality for Video Object Tracking
Generative-based Fusion Mechanism for Multi-Modal Tracking
RGB-T Tracking via Multi-Modal Mutual Prompt Learning
Unified Single-Stage Transformer Network for Efficient RGB-T Tracking
EANet: Enhanced Attribute-based RGBT Tracker Network
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
RGB-T Tracking Based on Mixed Attention
Visual Prompt Multi-Modal Tracking
Self-Supervised RGB-T Tracking with Cross-Input Consistency
Prompting for Multi-Modal Tracking
0/1 Deep Neural Networks via Block Coordinate Descent
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline
Dynamic Fusion Network for RGBT Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.