Deep Learning Framework for Density Estimation of Crowd Videos

Estimation crowd density from surveillance video is a significant research filed in the area of computer vision. Crowd density is one of the critical crowd monitoring parameters which can represent the space that is possessed by the crowd in the scene under surveillance. The space available for crowd gathering in a particular area may be limited, and the authorities need to restrict the crowd in that area within that predefined limit. If the density of the crowd exceeds that predefined limit, some congestion may occur in that scene. To avoid this congestion, we can incorporate the density parameter to the existing surveillance system to make the surveillance system more intelligent. In this paper, we propose a new crowd density estimation strategy using deep learning frameworks like convolution neural network and long short-term memory network. The experiment is tested with CUHK Crowd dataset, and the result is compared with existing crowd density estimation methods available in the literature.