Estimation of Crowd Density Using Image Processing Techniques with Background Pixel Model and Visual Geometry Group

Crowd density estimation in complex backgrounds using a single image has garnered significant attention in automatic monitoring systems. In this paper, we propose a novel approach to enhance crowd estimation by leveraging the Bayesian Loss algorithm in conjunction with monitoring points and datasets such as UCF-QNRF, UCF_CC_50, and ShanghaiTech. The proposed method is evaluated using standard metrics including Mean Square Error (MSE) and Mean Absolute Error (MAE). Experimental results demonstrate that the proposed method achieves significantly improved accuracy compared to existing estimation techniques. Specifically, the proposed technique showcases a 106.0 reduction in MSE and a 91.6 reduction in MAE over state-of-the-art methods, thereby validating its effectiveness in challenging crowd density estimation scenarios.