An Algorithm for Detection of Partially Camouflaged People

Several video analysis applications perform object detection using a background subtraction approach. Camouflage can be a serious problem for these applications, since the objects of interest may appear fragmented into small,disconnected pieces, with a dramatic negative impact on later processing phases such as classification or tracking. Nevertheless, this problem is largely underestimated in the literature. In this paper an effective, model-based solution is presented for the case of people detection. The proposed method acts as a post-processing phase, grouping together the fragmented blocks to restore the original object. A quantitative evaluation of the effectiveness of this method has been performed on real world videos from a video-surveillance application. The videos used for the experiments (with metadata) have been made publicly available on the Internet.