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<p>Visual object tracking is a fundamental problem in computer vision. Traditional tracking methods, which primarily rely on RGB imagery, often face difficulties in complex scenarios such as low resolution and background clutter. Hyperspectral imaging, which captures both spatial and spectral information across multiple narrow spectral bands, has emerged as a promising solution. However, hyperspectral tracking suffers from challenges including the complexity of spatial-spectral-temporal modeling, the high dimensionality of spectral bands, low spatial resolution, and limited training samples. In response, researchers have developed a variety of hyperspectral object tracking (HOT) methods, integrating innovations in band selection and advanced architectures such as Siamese networks, Transformers, and multimodal fusion-based approaches. Despite this progress, existing reviews primarily focus on specific sub-domains and provide limited coverage of recent developments in hyperspectral video camera technology and the associated data processing pipelines, including calibration and correction techniques. Therefore, this review aims to fill that gap by providing an in-depth and systematic overview of HOT. It examines the motivations driving this research, highlights recent technological and methodological developments, surveys publicly available datasets, and evaluates performance metrics. Finally, we discuss future research directions and prospects, offering a valuable resource for researchers and practitioners. Moreover, to support ongoing innovation, we have established a dedicated GitHub repository to document and continuously update relevant works: <a href="https://github.com/aamin0102/HOT-Review">https://github.com/aamin0102/HOT-Review</a>.</p>

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A comprehensive review of recent advancements in hyperspectral object tracking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月4日

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  • Mohammad Aminul Islam,
  • Fengchao Xiong,
  • Jun Zhou,
  • Wangzhi Xing,
  • Dong Bao,
  • Yongsheng Gao

摘要

Visual object tracking is a fundamental problem in computer vision. Traditional tracking methods, which primarily rely on RGB imagery, often face difficulties in complex scenarios such as low resolution and background clutter. Hyperspectral imaging, which captures both spatial and spectral information across multiple narrow spectral bands, has emerged as a promising solution. However, hyperspectral tracking suffers from challenges including the complexity of spatial-spectral-temporal modeling, the high dimensionality of spectral bands, low spatial resolution, and limited training samples. In response, researchers have developed a variety of hyperspectral object tracking (HOT) methods, integrating innovations in band selection and advanced architectures such as Siamese networks, Transformers, and multimodal fusion-based approaches. Despite this progress, existing reviews primarily focus on specific sub-domains and provide limited coverage of recent developments in hyperspectral video camera technology and the associated data processing pipelines, including calibration and correction techniques. Therefore, this review aims to fill that gap by providing an in-depth and systematic overview of HOT. It examines the motivations driving this research, highlights recent technological and methodological developments, surveys publicly available datasets, and evaluates performance metrics. Finally, we discuss future research directions and prospects, offering a valuable resource for researchers and practitioners. Moreover, to support ongoing innovation, we have established a dedicated GitHub repository to document and continuously update relevant works: https://github.com/aamin0102/HOT-Review.