A machine vision-based method for crowd density estimation and evacuation simulation

In this study, we propose a machine vision-based method for crowd density estimation and evacuation simulation to help reduce the occurrence of stampedes in crowded public places. The method consists of pedestrian detection model, pedestrian positioning algorithm, and cellular automata evacuation model (CAEM). In the pedestrian detection model, an adaptive 2D Gaussian kernel was used to generate crowd density heatmaps for crowd density estimation. The model was found to have achieved a real-time detection accuracy of 96.5% for real-time images of evacuation scenes after being trained on a dataset of 66,808 pedestrian images using the YOLOv3. The pedestrian positioning algorithm was developed to determine the coordinates of pedestrians in real-world scenarios. Experiments show that the average positioning errors in the x and y directions are 0.177 m and 0.176 m, respectively. The pedestrian coordinates were then input into the Python-based cellular automata evacuation model (CAEM) for evaluation simulation, which is compared with the simulation results by Path-finder, another piece of software that can deliver agent-based evacuation simulation. The difference in evacu-ation time calculated by the two models was found to be less than a second, which indicates a high degree of consistency.