Abstract Information about background appearance and previous tracked frames is significant for effectively discriminating a target from a complex scene. In this paper, we propose a novel tracker called temporal regularization and background-aware correlation filter (TRBACF) tracker based on the theory of correlation filter. In the proposed TRBACF tracker, an improved circular shift operation for collecting training samples is used to obtain more background information, which can enhance the discrimination ability of the learned correlation filters. Furthermore, to ensure the long-term tracking performance, a temporal regularization term is added to the appearance model of the classical correlation filter. The developed appearance model can take advantage of the similarity of the filters in the adjacent frames and improve the learned filter to be more adaptive to variations in the scene. Extensive experimental results on various challenging videos demonstrate that the proposed TRBACF tracker achieves superior accuracy than some state-of-the-art trackers.

中文翻译：

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基于时间正则化和背景意识的相关滤波器，

摘要 有关背景外观和先前跟踪帧的信息对于从复杂场景中有效区分目标非常重要。在本文中，我们提出了一种基于相关滤波器理论的新型跟踪器，称为时间正则化和背景感知相关滤波器（TRBACF）跟踪器。在提出的 TRBACF 跟踪器中，使用改进的循环移位操作收集训练样本以获得更多的背景信息，这可以增强学习到的相关滤波器的辨别能力。此外，为了确保长期跟踪性能，在经典相关滤波器的外观模型中添加了时间正则化项。开发的外观模型可以利用相邻帧中过滤器的相似性，并改进学习过滤器以更适应场景中的变化。对各种具有挑战性的视频的大量实验结果表明，所提出的 TRBACF 跟踪器比一些最先进的跟踪器具有更高的精度。