With the increase in the number of surveillance cameras being deployed globally, an important topic is person re-identification (Re-ID), which identifies the same person from multiple different angles and different directions across multiple cameras. However, because of the privacy issues involved in the identification of individuals, Re-ID systems cannot send the image data to cloud, and these data must be processed on edge servers. However, there has been a significant increase in computing resources owing to the processing of artificial intelligence (AI) algorithms through edge computing (EC). Consequently, the traditional AI Internet of Things (AIoT) architecture is no longer sufficient. In this study, we designed a Re-ID system at the AIoT EC gateway, which utilizes a microservice to perform Re-ID calculations on EC and balances efficiency with privacy protection. Experimental results indicate that this architecture can provide sufficient Re-ID computing resources to allow the system to scale up or down flexibly to support different scenarios and demand loads.