Detection of Abnormal Behaviour in a Surveillance Environment Using Control Charts

This paper introduces a new approach to unsupervised detection of abnormal sequences of images in video surveillance data. We leverage an online object detection method and statistical process control techniques in order to identify suspicious sequences of events. Our method assumes a training phase in which the spatial distribution of objects is learned, followed by a chart-based tracking process. We evaluate the performance of our method on a standard dataset and have implemented a publicly available opensource prototype.