Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks
This addresses the challenge of person tracking across large camera networks for surveillance applications, though it appears incremental as it combines existing problems rather than introducing a fundamentally new approach.
The paper tackles the joint problems of person re-identification and camera network topology inference in multi-camera networks, proposing a unified framework that shows promising results on both tasks and introduces a new annotated dataset called SLP.
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.