Although a wide variety of background subtraction methods has been proposed in recent years, none has been able to fully address multi-scale moving objects and dynamic background in real surveillance tasks. In this paper, a novel and effective background subtraction method, named regional multi-feature-frequency (RMFF), is proposed to detect multi-scale moving objects under dynamic background. Unlike many existing methods construct background model using simple multi-feature combinations, RMFF exploits the spatiotemporal cues of multi-feature as well as superpixels at each scale, thus allowing for more robust information to be exploited for background modeling. Specifically, the spatial relationship between pixels in a neighborhood and the frequencies of features over time are first exploited, enabling accurate detection of moving objects while ignoring most dynamic background changes. Then, the use of multi-scale superpixels for exploiting the structural information existing in real-world scenes further enhances robustness to multi-scale objects and environmental variations. Finally, an adaptive strategy is employed to dynamically adjust the foreground/background segmentation threshold for each region without user intervention. This adaptive threshold is defined for each region separately, and can adjust dynamically based on continuous monitoring of the background changes, thereby effectively reducing potential segmentation noise. Experiments on the 2014 version of the ChangeDetection.net dataset demonstrate that the proposed method outperforms the 12 state-of-the-art algorithms in terms of overall F-Measure and performs effectively in many complex scenes. Consequently, it is verified that the developed approach is feasible and useful for robust application in practical video surveillance.

尽管近年来提出了各种各样的背景减法方法，但没有一种方法能够完全解决实际监控任务中的多尺度运动物体和动态背景。在本文中，提出了一种新颖有效的背景减法方法，称为区域多特征频率（RMFF），用于检测动态背景下的多尺度运动物体。与使用简单的多特征组合构建背景模型的许多现有方法不同，RMFF 利用多特征的时空线索以及每个尺度的超像素，从而允许利用更强大的信息进行背景建模。具体来说，首先利用邻域中像素之间的空间关系和随时间变化的特征频率，能够准确检测移动物体，同时忽略大多数动态背景变化。然后，使用多尺度超像素来利用现实世界场景中存在的结构信息，进一步增强了对多尺度物体和环境变化的鲁棒性。最后，采用自适应策略动态调整每个区域的前景/背景分割阈值，无需用户干预。这个自适应阈值是为每个区域单独定义的，并且可以根据对背景变化的连续监测进行动态调整，从而有效地减少潜在的分割噪声。在 2014 版 ChangeDetection 上进行实验。net 数据集表明，所提出的方法在整体 F-Measure 方面优于 12 种最先进的算法，并且在许多复杂场景中有效执行。因此，验证了所开发的方法在实际视频监控中的稳健应用是可行和有用的。