Artificial Intelligence (AI) and Internet of Things (IoT) technologies have developed rapidly in recent years. AI on the Edge technology combined with IoT technology are very potential for smart city applications, the security protection is one of the very important problem in smart city. This study proposes a solution for detecting abnormal and dangerous activities using AI on the edge which can be applied in smart city applications. This project aims at developing a system which can detect abnormal and dangerous activities using Deep learning model on the edge computer. The video signal from the camera will be processed by embedded computer Jetson Nano, which is implemented with deep learning models to detect some abnormal and dangerous activities such as human without facemask in the SARS-CoV-2 pandemic areas or man with gun and knife in the city public areas..., the information of detected abnormal activities will be sent to cloud server through the IoT system. YOLOv5 deep learning model is selected to implement in this system, thousands of abnormal activities have been collected to train the model. A prototype abnormal and dangerous activities detection system has been designed and implemented in practical testing areas, which has very high accuracy detection result. Based on these initial results of the proposed solution we can develop some practical applications for smart city to detect and track different kinds of abnormal human activities in smart city for security issues. © 2021 IEEE.
Detecting Abnormal and Dangerous Activities Using Artificial Intelligence on the Edge for Smart City Application