Crowd Density Prediction

Authors:

J. Sai Gagan1, V. Tarun Kumar2, Ravi Kiran3, G. Vivekananda Reddy4, Prof. Ayush Pandey5

1,2,3 CSE Department of Computer Science Engineering(AI&ML), Sandip University, Nashik, Maharashtra, India.

⁴ CSE Department of Computer Science Engineering(AI&ML), Sandip University, Nashik, Maharashtra, India.

Abstract - Crowd density prediction is a critical task in modern public safety, urban planning, and large-scale event management. Accurately estimating the number of people and their spatial distribution enables authorities to prevent dangerous overcrowding and optimize resource deployment. This paper presents a hybrid crowd density prediction framework combining Convolutional Neural Networks (CNN) with Histogram of Oriented Gradients (HOG) feature descriptors. The CNN branch employs a modified VGG-16 backbone with dilated convolutions to capture high-level spatial representations, while the HOG branch extracts complementary gradient-based structural information from local image regions. Feature embeddings from both branches are fused through fully connected layers with dropout regularization to generate spatial density maps. The system is trained and validated on the ShanghaiTech Part A and Part B benchmark datasets. The proposed CNN+HOG hybrid achieves a Mean Absolute Error (MAE) of 75.4 and Root Mean Square Error (RMSE) of 112.3 on Part A, and MAE of 11.8, RMSE of 18.5 on Part B - outperforming pure CNN and HOG baselines. Training convergence analysis and error distribution studies confirm the robustness and generalization of the proposed approach across varying crowd densities and scene complexities.

KeyWords: Crowd density prediction, convolutional neural network (CNN), histogram of oriented gradients (HOG), deep learning, density map estimation, ShanghaiTech dataset, feature fusion.