Multi-Camera Multi-Person Tracking in Surveillance System

Abstract

Surveillance systems have become an integral part of modern security infrastructure. The ability to track multiple individuals across multiple cameras in real-time is crucial for the effectiveness of such systems. In this paper, we propose a multi-camera multi-person tracking system capable of accurately tracking multiple individuals across a network of cameras. Our proposed system utilizes a combination of computer vision and machine learning techniques to perform robust tracking in complex environments with varying lighting conditions, occlusions, and camera views. The system employs a deep learning-based object detection algorithm to detect individuals in each camera view and a multi-object tracking algorithm to associate and track the individuals across the camera network. To evaluate the performance of our proposed system, we conducted experiments on a publicly available dataset, and the results show the effectiveness of our system in achieving high accuracy and efficiency in multi-camera multi-person tracking. The proposed system is scalable and can be easily integrated with existing surveillance systems to enhance their tracking capabilities. Overall, our proposed multi-camera multi-person tracking system can provide an effective solution for the real-time tracking of individuals across a network of cameras, which can significantly enhance security and safety in various domains, such as transportation, public spaces, and critical infrastructure. © 2023 IEEE.