Adaptation of person re-identification models for on-boarding new camera(s)

Abstract

Abstract Existing approaches for person re-identification have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where one or multiple new cameras may be temporarily on-boarded into an existing system to get additional information or added to expand an existing network. To address such a very practical problem, we propose a novel approach for adapting existing multi-camera re-identification frameworks with limited supervision. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with newly introduced target camera(s), without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Third, we develop a target-aware sparse prototype selection strategy for finding an informative subset of source camera data for data-efficient learning in resource constrained environments. Our approach can greatly increase the flexibility and reduce the deployment cost of new cameras in many real-world dynamic camera networks. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised alternatives whilst being extremely efficient to compute.