Person Re-identification Based On Multiple Cameras

Person re-identification is an essential topic in computer vision.In this paper,we address the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images.We further introduce a global constraint-based matching process.It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process,hence achieving a more reliable matching score between images.Finally,we also extend our approach by introducing a multistructure scheme,which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair,so as to handle the spatial misalignments between individual images in a more precise way.Experimental results on various datasets demonstrate the effectiveness of our approach.