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Distributed and Unsupervised Cost-Driven Person Re-Identification

The problem of re-identify persons across single disjoint camera-pairs has received great attention from the community. Despite this, when the re-identification process has to be carried out on a large camera network a different approach has to be considered. In particular, existing approaches have neglected the importance of the network topology (i.e., the structure of the monitored environment) in such a process. To try filling such a gap, we propose a Distributed and Unsupervised Cost-Driven Person Re-Identification framework (DUPRe) which introduces the following contributions: (i) a camera matching cost to measure the re-identification performance between nodes of the network; (ii) a derivation of the distance vector algorithm which allows to learn the network topology hence to prioritize and limit the cameras inquired for the re-identification. Results on two benchmark datasets show that our solution brings to significant network-wise re-identification improvements.