Development of an energy-efficient cctv camera system for real-time human detection using YOLOv8 model

Abstract

Human recognition is widely used in variety of fields such as autonomous vehicles, surveillance field, automatons, assisting blind peoples and many more. Many machine learning (ML) and deep learning (DL) algorithms exist for video analysis the main motive of these algorithms is to find human in complicated image. The research presented in this paper focuses on the development of an energy-efficient, smart CCTV camera system for real-time human detection, utilizing the YOLOv8 (You Only Look Once) model. The problem addressed is the need for more advanced, autonomous surveillance systems capable of human detection under various background conditions, overcoming the limitations of traditional CCTV systems, which require constant manual monitoring. The proposed system was trained on the PASCAL VOC 2012 dataset and optimized through hyperparameter tuning, achieving high accuracy and real-time performance. Key results demonstrate that the YOLOv8 model, implemented on the NVIDIA Jetson Nano platform, offers remarkable accuracy, precision, and energy efficiency. It consistently detects human figures in real-time, even in non-ideal conditions like poor lighting or complex backgrounds. This success can be attributed to YOLOv8’s cross-stage partial network (CSPNet) architecture, which enhances its ability to process images quickly and accurately, ensuring it meets the demands of continuous surveillance. The distinguishing features of this system are its energy-efficient design and adaptability to diverse environmental conditions. These characteristics not only solve the challenge of real-time human detection but also make the system a robust and scalable solution for modern security and surveillance applications