Deep Learning Based Efficient Crowd Counting System

Estimation of crowd count is becoming crucial nowadays, as it can help in security surveillance, crowd monitoring, and management for different events. It is challenging to determine the approximate crowd size from an image of the crowd's density. Therefore in this research study, we proposed a multi-headed convolutional neural network architecture-based model for crowd counting, where we divided our proposed model into two main components: (i) the convolutional neural network, which extracts the feature across the whole image that is given to it as an input, and (ii) the multi-headed layers, which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd. We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model's performance. To analyze the results, we used two metrics Mean Absolute Error (MAE) and Mean Square Error (MSE), and compared the results of the proposed systems with the state-of-art models of crowd counting. The results show the superiority of the proposed system.