Real-Time Scalable System For Face Tracking In Multi-Camera

Abstract

Face detection and tracking have become increasingly popular in recent years. It has critical importance in security, defense, and robotics applications uses encountered in everyday life. For this purpose, many decision support or expert systems have been developed using artificial intelligence and machine learning. Thanks to the developments in the field of deep learning and hardware many effective and reliable face tracking systems have been realized. However there are still very few real-time scalable end-to-end systems. Also, the realization of this system on multiple cameras is a real challenge. In this study, a real-time, multi-camera, deep learning-based face tracking system has been developed. In the realized system, SCRFD model is used for face detection, ArcFace model is used for face recognition, and an updated DeepSORT algorithm is used for more stable face tracking. In addition, Apache Kafka stream processing system and Socket.IO bidirectional communication library were used to process multi-camera data in real-time and scalable. In the proposed system, when an image is input into the system, it can be displayed on the web page after approximately 127 ms