Advanced Crowd Density Estimation Using Hybrid CNN Models for Real-Time Public Safety Applications

Abstract

Abstract: This paper explores the use of Convolutional Neural Networks (CNN) and deep learning techniques for effective crowd density estimation in high-density environments such as public events, urban centers, and exhibition spaces. Existing methodologies face challenges, particularly in high-density scenarios where manual feature extraction methods struggle with occlusion, scale variation, and environmental noise. This research addresses these limitations by employing CNN-based frameworks, including a proposed multitask approach that integrates both detection and regression to improve crowd density estimation. Through a series of experiments using real-time crowd data, the model demonstrates significant improvements in accuracy, scalability, and computational efficiency compared to traditional methods. The proposed model also excels in dynamic environments, making it suitable for real-time applications in public safety and urban management. Results show a reduction in Mean Absolute Error (MAE) and Mean Square Error (MSE) metrics, validating the model's performance in complex, real-world conditions. This work contributes to the on-going development of intelligent systems for crowd management and public safety.