Learning Implicit Transfer for Person Re-identification

Tamar Avraham, Ilya Gurvich, Michael Lindenbaum, Shaul Markovitch

Abstract: This paper proposes a novel approach for pedestrian re-identification. Previous re-identification methods use one of 3 approaches: invariant features; designing metrics that aim to bring instances of shared identities close to one another and instances of different identities far from one another; or learning a transformation from the appearance in one domain to the other. Our implicit approach models camera transfer by a binary relation R = {(x,y)|x and y describe the same person seen from cameras A and B respectively}. This solution implies that the camera transfer function is a multi-valued mapping and not a single-valued transformation, and does not assume the existence of a metric with desirable properties. We present an algorithm that follows this approach and achieves new state-of-the-art performance.