An online graph-based anomalous change detection strategy for unsupervised video surveillance

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.