A Subspace Learning Approach to Multishot Person Reidentification

This paper addresses the challenging problem of multishot person reidentification (Re-ID) in real world uncontrolled surveillance systems. A key issue is how to effectively represent and process the multiple data with various appearance information due to the variations of pose, occlusions, and viewpoints. To this end, this paper develops a novel subspace learning approach, which pursues regularized low-rank and sparse representation for multishot person Re-ID. For the images of a person crossing a certain camera, we assume that the appearances of those subset images with similar viewpoints against a camera draw from the same low-rank subspace, and all the images of a person under a camera lie on a union of low-rank subspaces. Based on this assumption, we propose to learn a nonnegative low-rank and sparse graph to represent the person images. Moreover, the recurring pattern prior is integrated into our model to refine the affinities among images. Extensive experiments on four public benchmark datasets yield impressive performance by improving 22.9% on imagery library for intelligent detection systems video re identification (iLIDS-VID), 42.4% on person RE-ID (PRID) dataset 2011, 39.7% and 30.6% on speech, audio, image, and video technology-SoftBio camera 3/8 and camera 5/8, respectively, and 1.6% on motion analysis and re identification set compared to the state-of-the-art methods.