  Person re-identification in large-scale multi-camera networks is a
challenging task because of the spatio-temporal uncertainty and high complexity
due to large numbers 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. In this paper, we propose a
unified framework which jointly solves both person re-id and camera network
topology inference problems. The proposed framework takes general multi-camera
network environments into account. To effectively show the superiority of the
proposed framework, we also provide a new person re-id dataset with full
annotations, named SLP, captured in the synchronized multi-camera network.
Experimental results show that the proposed methods are promising for both
person re-id and camera topology inference tasks.
