Abstract—Different technologies are employed to detect and count people in various situations but crowd counting system based on computer vision is one of the best choices due to a number of advantages. These include accuracy, flexibility, cost and acquiring people distribution information. Crowd counting system based on computer vision can use closed circuit television cameras (CCTV) that have already become ubiquitous and their uses are increasing. This paper aims to develop crowd counting system that can be incorporated with existing CCTV cameras. In this paper, the extracted low-level features in a frame-to-frame analysis are processed using regression technique to estimate the number of people. Two complex scenes and environments are used to evaluate the performances of the proposed system. The results have shown that the proposed system can achieve good performance in terms of the mean absolute error (MAE) and mean squared error (MSE).
Index Terms—People counting, regression technique, CCTV cameras, computer vision.
Z. Q. H. Al-Zaydi is with the School of Engineering, University of Portsmouth, Portsmouth, UK, and is also with the Biomedical Engineering Department, University of Technology, Baghdad, Iraq (e-mail: firstname.lastname@example.org).
B. Vuksanovic is with the School of Engineering, University of Portsmouth, Portsmouth, UK (e-mail: email@example.com).
I. Q. Habeeb is with the School of Engineering, University of Information Technology & Communications, Baghdad, Iraq (e-mail: firstname.lastname@example.org).
Cite: Zeyad Al-Zaydi, Branislav Vuksanovic, and Imad Habeeb, "Image Processing Based Ambient Context-Aware People Detection and Counting," International Journal of Machine Learning and Computing vol. 8, no. 3, pp. 268-273, 2018.