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) onfeatures extracted from a video stream. CRFs estimatea state sequence, given a feature sequence. To detectintrusions, we analyze this state sequence. CRFs areusually trained in a supervised manner. Here, we espe-cially propose a new training algorithm for CRFs basedon expectation maximization, which can be used withunlabeled data. We apply the resulting trained CRFto separate normal activities from suspicious behavior.We have successfully tested our algorithm on 169 se-quences.
|Number of pages||4|
|Publication status||Published - 01.08.2013|
|Event||MVA2013 IAPR International Conference on Machine Vision Applications - Kyoto, Japan|
Duration: 20.05.2013 → 23.05.2013
|Conference||MVA2013 IAPR International Conference on Machine Vision Applications|
|Period||20.05.13 → 23.05.13|