Change detection has received extensive attention because of its realistic significance and broad application fields. However, none of the existing change detection algorithms can handle all scenarios and tasks so far. Different from the most of contributions from the research community in recent years, this paper does not work on designing new change detection algorithms. We, instead, solve the problem from another perspective by enhancing the raw detection results after change detection. As a result, the proposed method is applicable to various kinds of change detection methods, and regardless of how the results are detected. In this paper, we propose Fast Spatiotemporal Tree Filter (FSTF), a purely unsupervised detection method, to enhance coarse binary detection masks obtained by different kinds of change detection methods. In detail, the proposed FSTF has adopted a volumetric structure to effectively synthesize spatiotemporal information of the same target from the current time and history frames to enhance detection. The computational complexity analyzed in the view of graph theory also show that the fast realization of FSTF is a linear time algorithm, which is capable of handling efficient on-line detection tasks. Finally, comprehensive experiments based on qualitative and quantitative analysis verify that FSTF-based change detection enhancement is superior to several other state-of-the-art methods including fully connected Conditional Random Field (CRF), joint bilateral filter, and guided filter. It is illustrated that FSTF is versatile enough to also improve saliency detection as well as semantic image segmentation.

中文翻译：

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用于增强图像变化检测的时空树过滤。


变化检测因其现实意义和广泛的应用领域而受到广泛关注。然而，迄今为止，现有的变化检测算法都无法处理所有场景和任务。与近年来研究界的大多数贡献不同，本文并不致力于设计新的变化检测算法。相反，我们通过增强变化检测后的原始检测结果，从另一个角度解决问题。因此，所提出的方法适用于各种变化检测方法，并且无论结果是如何检测的。在本文中，我们提出了快速时空树过滤器（FSTF），一种纯粹的无监督检测方法，以增强通过不同类型的变化检测方法获得的粗略二值检测掩模。具体来说，所提出的FSTF采用了体积结构来有效地从当前时间和历史帧中合成同一目标的时空信息以增强检测。从图论角度分析计算复杂度也表明，FSTF的快速实现是一种线性时间算法，能够处理高效的在线检测任务。最后，基于定性和定量分析的综合实验验证了基于 FSTF 的变化检测增强优于其他几种最先进的方法，包括全连接条件随机场（CRF）、联合双边滤波器和引导滤波器。结果表明，FSTF 具有足够的通用性，还可以改进显着性检测和语义图像分割。