Crowd Density Estimation Based on a Modified Multicolumn Convolutional Neural Network

Abstract

Crowd management has been a topic of concern for many years because accidents frequently occur in situations with a high crowd density. With only a finite amount of space available during shows, protests, or other special occasions, a high crowd density can present a clear danger for those in the area. Considering these challenges, we employed and modified a three-tier multicolumn convolutional neural network (MCNN) system architecture to precisely estimate crowd density. We distinguished three regions from the near to far field to produce a crowd density map. Based on the MCNN system architecture, we detected changes in the size of a crowd according to a distance measure and examined additional features that can be incorporated to demonstrate their effects on crowd density maps. Examining these features using the Shanghaitech dataset demonstrated that compared with the native MCNN, the accuracy of estimating crowd counting by using our proposed method increased by 22.97% and 18.64% in terms of mean absolute error (MAE) and mean square error (MSE), respectively. A performance comparison with other state-of-the-art methods was also made. From this, we can infer that the proposed system is compatible with the other listed methods and is worthy of further investigation.