https://github.com/BUGPLEASEOUT/LasHeR
공식 코드LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion
Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation
Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking
Unified Single-Stage Transformer Network for Efficient RGB-T Tracking
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline
Multi-modal Visual Tracking: Review and Experimental Comparison
Multi-modal Visual Tracking: Review and Experimental Comparison
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.