Real-Time Human Detection and Counting System Using Deep Learning Computer Vision Techniques
Hamam Mokayed, Tee Zhen Quan, Lama Alkhaled, V. Sivakumar
Abstract
Targeting the current Covid 19 pandemic situation, this paper identifies the need of crowd management. Thus, it proposes an effective and efficient real-time human detection and counting solution specifically for shopping malls by producing a system with graphical user interface and management functionalities. Besides, it comprehensively reviews and compares the existing techniques and similar systems to select the ideal solution for this scenario. Specifically, advanced deep learning computer vision techniques are decided by using YOLOv3 for detecting and classifying the human objects with DeepSORT tracking algorithm to track each detected human object and perform counting using intrusion line judgment. Additionally, it converts the pretrained YOLOv3 into TensorFlow format for better and faster real-time computation using graphical processing unit instead of using central processing unit as the traditional target machine. The experimental results have proven this implementation combination to be 91.07% accurate and real-time capable with testing videos from the internet to simulate the shopping mall entrance scenario. Received: 7 September 2022 | Revised: 27 September 2022 | Accepted: 11 October 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.