People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
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In this paper, we present a new person re-identification method based on appearance classification and silhouette part segmentation. In crowded areas, heads are considered as most apparent parts, hence the typical advantage of using the skeleton graph for the head detection and location of people after partial occlusion. The appearance classification consists in characterizing the appearance of a person into two classes, the frontal and the back appearance, using head detector and the orthogonal iteration algorithm for head pose estimation. The silhouette part segmentation divides the silhouette into three horizontal parts, ideally corresponding to head, torso and legs using skeleton graph and head detector. Our approach is robust to real world situations, in particular to variations in scales, human pose, illumination and clothes appearance changes. It also allows to reduce the confusion cases among people appearance and the amount of falsely matches.