Review and Analysis of RGBT Single Object Tracking Methods: A Fusion Perspective

作者：Zhihao Zhang, Jun Wang, Shengjie Li, Lei Jin, Hao Wu, Jian Zhao, Bo Zhang · 发表于：ACM Transactions on Multimedia Computing Communications and Applications · 年份：2024 · DOI：10.1145/3651308 · 被引用次数：12 · 研究领域：Video Surveillance and Tracking Methods、Impact of Light on Environment and Health、Infrared Thermography in Medicine

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and pr...