Passenger flow counting method based on deep learning in vertical visual angle

The invention provides a passenger flow counting method based on deep learning in a vertical visual angle. According to the method, passenger flow statistics is performed in the vertical visual angle. Compared with an oblique photographing visual angle, the visual angle realizes easier coping with conditions such as market, supermarket and subway with high passenger density. The method provides head and shoulder detection by means of deep learning. Strong learning capability of deep learning is utilized. Background modeling and foreground extraction on a video are not required, and passenger cutting on the foreground is not required. Head information and shoulder information can be more accurately detected with higher robustness. A deep convolutional characteristic is utilized for performing matched tracking. Compared with manually designed characteristics such as HOG and LBP, the deep convolutional characteristic has better expression capability and can better cope with various scenes. The passenger flow counting method directly use the deep convolutional characteristic of a certain layer for matching, thereby preventing repeated characteristic calculation and realizing high time saving effect.