Due to various accidents and crime threats to an unspecified number of people, many surveillance technologies have been studied as an interest in individual security continues to increase throughout society. In particular, intelligent video surveillance technology is one of the most active research areas in the field of surveillance; this popularity has been spurred by recent advances in computer vision/image processing and machine learning. The main goal is to automatically detect, recognize, and analyze objects of interest from collected sensor information and then efficiently extract/utilize this useful information, such as by detecting abnormal events or intruders and recognizing objects. Anomalous event detection is a key component of security, and many existing anomaly detection algorithms rely on a foreground subtraction process to detect changes in the foreground scene. By comparing input image frames with a reference image, changed areas of the image can be efficiently detected. However, this technique can be insensitive to static changes and has difficulties in noisy environments since it depends on a reference image. We propose a new strategy for improved dynamic/static change detection that complements the weak points of existing detection methods, which have low robustness in noisy environments. To achieve this goal, we employed a self-organizing map (SOM) for data clustering and regarded the cluster distribution of neurons, represented by the weight of the optimized SOM, as a directed graph problem. We then applied the shortest path algorithm to recognize anomalous events. The real-time monitoring capability of the proposed change detection system was verified by applying it to self-produced test data and the CDnet-2014 dataset. This system showed robustness against noise that was superior to other surveillance systems in various environments.

中文翻译：

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基于在线图的异常变化检测策略，用于无监督视频监控

由于各种事故和犯罪威胁的人数不明，因此人们对许多监视技术进行了研究，这是因为整个社会对个人安全的兴趣不断提高。特别地，智能视频监视技术是监视领域中最活跃的研究领域之一。计算机视觉/图像处理和机器学习的最新进展推动了这种普及。主要目标是从收集的传感器信息中自动检测，识别和分析感兴趣的对象，然后有效地提取/利用此有用信息，例如通过检测异常事件或入侵者并识别对象。异常事件检测是安全性的关键组成部分，并且许多现有的异常检测算法都依赖于前景减法过程来检测前景场景中的变化。通过将输入图像帧与参考图像进行比较，可以有效地检测图像的变化区域。但是，该技术可能对静态变化不敏感，并且在嘈杂的环境中存在困难，因为它依赖于参考图像。我们提出了一种新的策略来改进动态/静态变化检测，以弥补现有检测方法的弱点，该方法在嘈杂的环境中具有较低的鲁棒性。为了实现此目标，我们采用了自组织映射（SOM）进行数据聚类，并将以优化SOM的权重表示的神经元的聚类分布视为有向图问题。然后，我们应用了最短路径算法来识别异常事件。通过将其应用于自行生成的测试数据和CDnet-2014数据集，验证了所提议的变更检测系统的实时监控能力。该系统显示出强大的抗噪声能力，在各种环境中均优于其他监视系统。