The tracking community is increasingly focused on RGBT tracking, which leverages the complementary strengths of corresponding visible light and thermal infrared images. The most well-known RGBT trackers, however, are unable to balance performance and...
The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. T...
The fusion tracking of RGB and thermal infrared image (RGBT) has attracted widespread interest within target tracking by leveraging the complementing benefits of information from both visible and thermal infrared modalities, but achieving robustness...
The development of single-modality target tracking based on visible light has been limited in recent years because visual light images are highly susceptible to environmental and lighting influences. Thermal infrared images can well compensate for th...
RGBT tracking holds irreplaceable value in unmanned aerial vehicle (UAV) ground observation missions, effectively supporting scenarios such as nighttime monitoring and low-altitude reconnaissance. However, existing frameworks based on CNNs or Transfo...
Advancements in deep learning and infrared sensors have facilitated the integration of RGB-thermal (RGBT) tracking technology in computer vision. However, contemporary RGBT tracking methods handle complex image data, resulting in inference procedures...
It is difficult to achieve all-weather visual object tracking in an open environment only utilizing single modality data input. Due to the complementarity of RGB and thermal infrared (TIR) data in various complex environments, a more robust object tr...
Visual object tracking using visible light images and thermal infrared images, named RGB-T tracking, has recently attracted increasing attention in the tracking community. Deep neural network-based methods becoming the most popular RGB-T trackers, st...
RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tra...
The RGB and thermal (RGB-T) object tracking task is challenging, especially with various target changes caused by deformation, abrupt motion, background clutter and occlusion. It is critical to employ the complementary nature between visual RGB and t...
In the field of drone-based object tracking, utilization of the infrared modality can improve the robustness of the tracker in scenes with severe illumination change and occlusions and expand the applicable scene of the drone object tracking task. In...
Object tracking in RGB-thermal (RGB-T) videos is increasingly used in many fields due to the all-weather and all-day working capability of the dual-modality imaging system, as well as the rapid development of low-cost and miniaturized infrared camera...
RGB and thermal visual tracking is a highly effective solution for complex environments. However, existing multimodal trackers still face significant limitations. They often apply uniform fusion weights across the entire image. They also ignore the d...
Single-object tracking has progressed rapidly, yet it remains fragile under low illumination, occlusion, and background clutter. RGB-Thermal (RGB-T) tracking improves robustness via modality complementarity, yet many existing trackers do not dynamica...
Visual target tracking is a fundamental task in computer vision. Combining multi-modal information with tracking leverages complementary information, which improves the precision and robustness of trackers. Traditional multi-modal tracking methods ty...