Person Reidentification in a Distributed Camera Network Framework
Plenty of research has been conducted to obtain the best reidentification performance between a single camera-pairs. None of the current approaches has addressed the reidentification in a camera network by considering the network topology (i.e., the structure of the monitored environment). We introduce a distributed network person reidentification framework which introduces the following contributions. 1) a camera matching cost to measure the reidentification performance between nodes of the network and 2) a derivation of the distance vector algorithm which allows to learn the network topology thus to prioritize and limit the cameras inquired for the matching of the probe. Results on three benchmark datasets show that the network topology can be learned in an unsupervised fashion and network-wise reidentification performance improves. As a side effect, we obtain that the communication bandwidth usage is reduced.