Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks

TL;DR

This paper introduces a unified framework that simultaneously addresses person re-identification and camera network topology inference in large-scale multi-camera systems, supported by a new annotated dataset and promising experimental results.

Contribution

The paper presents a novel integrated approach for joint person re-id and camera topology inference, along with a new dataset for evaluation.

Findings

The framework effectively improves person re-id accuracy.

It successfully infers camera network topology.

Experimental results demonstrate the method's promising performance.

Abstract

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.