Efficient Person Reidentification for IoT-Assisted Cyber–Physical Systems

Abstract

The main objective of this study is to propose a cyber-physical system (CPS) based person re-identification framework for smart surveillance. The Internet of Things (IoT) based interconnected vision sensors in smart cities are considered essential elements of a CPS, and contribute significantly to urban security. However, the re-identification of targeted persons using emerging edge AI techniques still faces certain challenges. To improve efficiency at the edge and overcome the traditional sensing of video cameras, we employed an AI-based person re-identification framework for CPS that is functional in IoT environments. In addition, we present dual attention dilated network (DADNet), which integrates an energy-efficient convolutional neural network (CNN) with a self-attention module to substantially improve the person matching probability. Further, we applied dual feature fusion to intelligently integrate discriminative and robust features using early and late fusion strategies that allow DADNet to significantly consider the foreground and marginally utilize the background information. Furthermore, we impose diversity orthogonality regularization over several CNN layers, which boosts the performance of DADNet, resulting in an appropriate usage over IoT networks. A comprehensive set of ablation studies, comparison with other state-of-the-art approaches, and a time complexity analysis confirm the strength of our DADNet for re-identification tasks in AI-enabled IoT settings that are well-suited for a CPS.