Crowd counting model for evacuation scenarios based on a cascaded CNN

Accurate crowd counts during evacuations can support real-time optimization of evacuation routes and scheduling of emergency resources. This study estimates the number of occupants in an evacuation passageway by setting a classification level and personnel density together in a cascaded convolutional neural network (CNN) crowd counting model based on analyses of existing methods. The method avoids the loss of image information and over fitting in the convolution process. The model estimates the real-time crowd count in crowded situations by learning the relationship between the number and the position of occupants in the image and by changing the image features. The model was implemented on the PyTorch platform with an identification accuracy for the validation set (612 photos) of 84.2% and for the test set (182 photos) of 83.6%, which shows that this method can accurately predict the number of evacuees in a monitoring screen.