Clothing Attributes Assisted Person Reidentification

Abstract

Person reidentification across nonoverlapping camera views is a rather challenging task. Due to the difficulties in obtaining identifiable faces, clothing appearance becomes the main cue for identification purposes. In this paper, we present a comprehensive study on clothing attributes assisted person reidentification. First, the body parts and their local features are extracted for alleviating the pose-misalignment issue. A latent support vector machine (LSVM)-based person reidentification approach is proposed to describe the relations among the low-level part features, middle-level clothing attributes, and high-level reidentification labels of person pairs. Motivated by the uncertainties of clothing attributes, we treat them as real-value variables instead of using them as discrete variables. Moreover, a large-scale real-world dataset with 10 camera views and about 200 subjects is collected and thoroughly annotated for this paper. The extensive experiments on this dataset show: 1) part features are more effective than features extracted from the holistic human bounding boxes; 2) the clothing attributes embedded in the LSVM model may further boost reidentification performance compared with support vector machine without clothing attributes; and 3) treating clothing attributes as real-value variables is more effective than using them as discrete variables in person reidentification.