Smart Surveillance System Real-Time Multi-Person Multi-Camera Tracking at the Edge

Abstract

Abstract: Nowadays, new Artificial Intelligence (AI) and Deep Learning based processing methods are replacing traditional computer vision algorithms. On the other hand, the rise of the Internet of Things (IoT) and edge computing, has led to many research works that propose distributed video-surveillance systems based on this notion. Usually, the advanced systems process massive volumes of data in different computing facilities. Instead, this paper presents a system that incorporates AI algorithms into low-power embedded devices. The computer vision technique, which is commonly used in surveillance applications, is designed to identify, count, and monitor people's movements in the area. A distributed camera system is required for this application. The proposed AI system detects people in the monitored area using a MobileNet-SSD architecture. This algorithm can keep track of people in the surveillance providing the number of people present in the frame. The proposed framework is both privacy-aware and scalable supporting a processing pipeline on the edge consisting of person detection, tracking and robust person re-identification. The expected results show the usefulness of deploying this smart camera node throughout a distributed surveillance system. Keywords: Edge Analytics, Person detection, Person re-identification, Deep learning, embedded systems, artificial intelligence