Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of the high tracking efficiency. However, CF-based algorithms usually failed to track objects in complex environments. Therefore, this article proposes a fuzzy detection strategy to prejudge the tracking result. If the prejudge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. During testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed.

中文翻译：

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复杂环境下模糊检测辅助实时鲁棒视觉跟踪


如今，新一代人工智能带来了计算机视觉（CV）等多个新的研究领域。因此，作为计算机视觉基础的目标跟踪一直是研究的热点领域。基于相关滤波器（CF）的算法因其跟踪效率高而成为实时跟踪算法的基础。然而，基于CF的算法通常无法跟踪复杂环境中的目标。因此，本文提出一种模糊检测策略来预判跟踪结果。如果预判断过程确定当前帧的跟踪结果不够好，则使用存储的目标模板进行后续跟踪，以避免模板污染。在OTB100数据集上进行测试时，实验结果表明，所提出的辅助检测策略在保证跟踪速度的同时，提高了复杂环境下跟踪的鲁棒性。