Automated Intrusion Detection for Video Surveillance Using Conditional Random Fields

In this paper, we propose a method for intrusion de-tection in a video surveillance scenario. For this pur-pose, we train a conditional random field (CRF) on features extracted from a video stream. CRFs estimate a state sequence, given a feature sequence. To detect intrusions, we analyze this state sequence. CRFs are usually trained in a supervised manner. Here, we espe-cially propose a new training algorithm for CRFs based on expectation maximization, which can be used with unlabeled data. We apply the resulting trained CRF to separate normal activities from suspicious behavior. We have successfully tested our algorithm on 169 se-quences.