Abstract

Person re-identifification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fifixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identifification problem, where a new camera may be temporarily inserted into an existing system to get additional information. To address such a novel and very practical problem, we propose an unsupervised adaptation scheme for re-identifification models in a dynamic camera network. First, we formulate a domain perceptive re-identifification method based on geodesic flflow kernel that can effectively fifind the best source camera (already installed) to adapt with a newly introduced target camera, without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identifification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Extensive experiments on four benchmark datasets demonstrate that the proposed approach signifificantly outperforms the state-ofthe-art unsupervised learning based alternatives whilst being extremely effificient to compute.