An embedded platform approach to privacy-centric person re-identification

Abstract

Systems capable of intelligently monitoring the traffic of people at entrances to enclosed areas enable a variety of useful applications such as improved retail store analytics. However, the real-world implementation of such a system is typically hindered by computationally expensive algorithms and privacy concerns. In this paper, a low-cost privacy-sensitive intelligent monitoring system based on an embedded platform is presented. The key components of the system include a people classification model and a people re-identification model. A detailed description of the optimisation of these components is presented. The developed system is able to detect people entering/exiting a closed area with an accuracy above 99% in real-time. In addition, the system is able to achieve re-identification accuracy above 93% in under 0.7 seconds on an embedded system. Data collected by the system was used for training and it was tested under real-world conditions.