  Person re-identification is the task of recognizing or identifying a person
across multiple views in multi-camera networks. Although there has been much
progress in person re-identification, person re-identification in large-scale
multi-camera networks still remains a challenging task because of the large
spatio-temporal uncertainty and high complexity due to a large number of
cameras and people. To handle these difficulties, additional information such
as camera network topology should be provided, which is also difficult to
automatically estimate, unfortunately. In this study, we propose a unified
framework which jointly solves both person re-identification and camera network
topology inference problems with minimal prior knowledge about the
environments. The proposed framework takes general multi-camera network
environments into account and can be applied to online person re-identification
in large-scale multi-camera networks. In addition, to effectively show the
superiority of the proposed framework, we provide a new person
re-identification dataset with full annotations, named SLP, captured in the
multi-camera network consisting of nine non-overlapping cameras. Experimental
results using our person re-identification and public datasets show that the
proposed methods are promising for both person re-identification and camera
topology inference tasks.
