Efficient Crowd Density Estimation: Fusion of Faster CNN and Vision Transformer

Abstract Accurate crowd density estimation in densely populated areas is essential for applications such as urban planning, security, and event management. Traditional methods often fall short in terms of accuracy and efficiency, especially when handling large datasets and real-time demands. This study introduces an efficient approach to crowd density estimation by integrating a rapid Convolutional Neural Network (CNN) with a Vision Transformer (ViT) architecture.Our method capitalizes on the strengths of both models to achieve enhanced performance. The fast CNN effectively captures local features and spatial details, while the Vision Transformer excels at identifying long-range dependencies and global context. By fusing these models, we create a synergistic effect that significantly improves crowd density estimation accuracy.We validate our approach through experiments on several benchmark datasets, covering both indoor and outdoor scenes with varying crowd densities. The results indicate that our method surpasses existing techniques in terms of accuracy while also maintaining high computational efficiency. We further demonstrate the scalability of our approach by testing its performance on large-scale datasets, confirming its suitability for real-world applications. Additionally, we explore the interpretability of the fused model, showing how the combination of CNN and Vision Transformer features enhances crowd density estimation. In summary, our proposed fusion method offers a promising solution for efficient and accurate crowd density estimation, with broad implications for practical applications in crowd analysis and management.