Hyperspectral images, with their many spectral bands, provide a rich source of material information about an object that can be effectively used for object tracking. However, many trackers in this domain rely on detection-based techniques, which often perform suboptimally in challenging scenarios such as managing occlusions and distinguishing objects in cluttered backgrounds. This underperformance is primarily due to the presence of multiple spectral bands and the inability to leverage this abundance of data for effective tracking. Additionally, the scarcity of annotated hyperspectral videos and the absence of comprehensive temporal information exacerbate these difficulties, further limiting the effectiveness of current tracking methods. To address these challenges, this article introduces the novel Hy-Tracker framework, designed to bridge the gap between hyperspectral data and state-of-the-art object detection methods. Our approach leverages the strengths of YOLOv7 for object tracking in hyperspectral videos, enhancing both accuracy and robustness in complex scenarios. The Hy-Tracker framework comprises two key components. We introduce a hierarchical attention for band selection (HAS-BS) that selectively processes and groups the most informative spectral bands, thereby significantly improving detection accuracy. Additionally, we have developed a refined tracker that refines the initial detections by incorporating a classifier and a temporal network using gated recurrent units (GRUs). The classifier distinguishes similar objects, while the temporal network models temporal dependencies across frames for robust performance despite occlusions and scale variations (SVs). Experimental results on hyperspectral benchmark datasets demonstrate the effectiveness of Hy-Tracker in accurately tracking objects across frames and overcoming the challenges inherent in detection-based hyperspectral object tracking (HOT).

中文翻译：

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Hy-Tracker：一种提高高光谱视频中对象跟踪效率和准确性的新型框架


高光谱图像具有许多光谱带，提供了有关物体的丰富材料信息源，可有效用于物体跟踪。然而，该领域的许多跟踪器依赖于基于检测的技术，这些技术在具有挑战性的场景中通常表现不佳，例如管理遮挡和区分杂乱背景中的对象。这种表现不佳的主要原因是存在多个光谱带以及无法利用如此丰富的数据进行有效跟踪。此外，带注释的高光谱视频的稀缺和缺乏全面的时间信息加剧了这些困难，进一步限制了当前跟踪方法的有效性。为了应对这些挑战，本文介绍了新颖的 Hy-Tracker 框架，旨在弥合高光谱数据和最先进的物体检测方法之间的差距。我们的方法利用 YOLOv7 的优势进行高光谱视频中的对象跟踪，从而提高复杂场景中的准确性和鲁棒性。 Hy-Tracker 框架包含两个关键组件。我们引入了波段选择的分层关注（HAS-BS），它有选择地处理和分组信息最丰富的光谱波段，从而显着提高检测精度。此外，我们还开发了一种改进的跟踪器，通过结合分类器和使用门控循环单元（GRU）的时间网络来改进初始检测。分类器区分相似的对象，而时间网络对跨帧的时间依赖性进行建模，以实现稳健的性能，尽管存在遮挡和尺度变化 (SV)。 高光谱基准数据集的实验结果证明了 Hy-Tracker 在跨帧准确跟踪目标方面的有效性，并克服了基于检测的高光谱目标跟踪 (HOT) 固有的挑战。