Guiding Eyes: A YOLOv3-Based Approach for Human Detection and Prevention
Ayaz Ali, Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro Pakistan.
Atif Nawaz, Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro Pakistan.
Asim Irfan, Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro Pakistan.
Aaqib Ali Sahito, Department of Telecommunication Engineering, University of Sindh, Jamshoro, Pakistan.
Corresponding Author:
Ayaz Ali (ayazalilakho2@gmail.com)
Abstract:
In recent years, the development of intelligent autonomous surveillance applications has greatly benefited from the integration of object detection techniques. Among these techniques, human detection has emerged as a crucial component in surveillance systems, enabling the detection and monitoring of suspicious activities. With the rising crime rates, the demand for enhanced security measures has grown significantly. In this research paper, we propose a security system that utilizes YOLOv3, a state-of-the-art object detection model, for human detection. Our system is designed to instantly notify us when humans are detected, ensuring a swift response to potential security threats. By employing email and text message notifications, our system promptly alerts us of human presence. Through extensive evaluation, our proposed network demonstrates impressive performance in detecting humans, regardless of their proximity to the camera. Our application outperforms existing surveillance detection systems, further enhancing the effectiveness of security monitoring.
Keywords:
Yolov3; Security; Human Detection; Object Detection; Image-Processing; Computer Vision.