Rgb-T Tracking
14 papers with code • 4 benchmarks • 2 datasets
RGBT tracking, or RGB-Thermal tracking, is a sophisticated method utilized in computer vision for tracking objects across both RGB (Red, Green, Blue) 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.
Most implemented papers
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
Current event-/frame-event based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performance of existing tracking algorithms in these scenarios remains unclear.
Multi-modal Visual Tracking: Review and Experimental Comparison
Visual object tracking, as a fundamental task in computer vision, has drawn much attention in recent years.
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking
The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively.
Attribute-Based Progressive Fusion Network for RGBT Tracking
RGBT tracking usually suffers from various challenging factors of fast motion, scale variation, illumination variation, thermal crossover and occlusion, to name a few.
Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking
To address this problem, this paper presents a novel approach to suppress background effects for RGB-T tracking.
Multi-Modal Fusion for End-to-End RGB-T Tracking
Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities.
LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking
RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods.
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline
With the popularity of multi-modal sensors, visible-thermal (RGB-T) object tracking is to achieve robust performance and wider application scenarios with the guidance of objects' temperature information.
Bridging Search Region Interaction With Template for RGB-T Tracking
To alleviate these limitations, we propose a novel Template-Bridged Search region Interaction (TBSI) module which exploits templates as the medium to bridge the cross-modal interaction between RGB and TIR search regions by gathering and distributing target-relevant object and environment contexts.
Visual Prompt Multi-Modal Tracking
To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters.