Speedy Vision-based Human Detection Using Lightweight Deep Learning Network

Abstract

Person detection plays a role as the initial system of video surveillance analysis with various implementations, such as activity analysis, person re-id, behavior analysis, and tracking analysis. The demand for efficient models drives a deep learning architecture with a superficial structure that can operate in real-time. You look only once (YOLO) object detection has been presented as an accurate detector that can operate in real-time. The speed limitation, huge computation cost, and abundant parameters still leave vital issues to improve the efficiency of this architecture. Lightweight human detection is proposed by utilizing the YOLOv5n framework. Modifying layer depth promotes a detection system that can operate fast and without stuttering. As a result, the proposed detector has satisfactory performance and is competitive with existing models. It achieves a mAP of 45.2%, closely competing with other person detectors. Additionally, it can run fast without stumbling at 26 frames per second. The detector's speed offers the advantage of this work that it can be feasibly implemented on a cpu device without a graphics accelerator.