An automatic video surveillance indexing based on facial feature descriptors

Automatic surveillance video footage indexing is much more desirable while providing an assistive tool for personnel security. Since the most relevant object that attracts our attention in surveillance videos is human face, we focus in this paper on building a system for indexing surveillance videos based on human face features. The proposed system has three main stages: Video Surveillance Summarisation, Face Detection, and Facial Feature Descriptors, and Indexing. A keyframe selection technique based on local foreground entropy is used for video surveillance summarisation. In the Face Detection stage, a skin color based method using measurements derived from the color-space components of the keyframe is used to locate eye, mouth and face boundary. Subsequently, SURF algorithm is applied to extract the feature descriptors of interest point from the detected face region. These descriptors are then indexed using vocabulary tree. The integration of the above-mentioned methods that are all good in their results, have made our overall system robust and efficient. Therefore, good results have been obtained while testing in ChokePoint public dataset contains 48 video sequences with a total of 179 349 frames including 64 204 face images.