Passenger flow estimation based on convolutional neural network in public transportation system

Automatic passenger flow estimation is very useful in public transportation system, which can improve the efficiency of public transportation service by optimizing the route plan and traffic scheduling. However, this task usually encounters many challenges in public transportation system, such as low resolution, background clutter, variation of illumination, pose and scale, etc. In this paper we propose a passenger counting system based on the convolutional neural network (CNN) and the spatio-temporal context (STC) model, where the CNN model is used to detect the passengers and the STC model is used to track the moving head of each passenger, respectively. Different from the traditional hand-engineered representation methods, our method uses CNN to automatically learn the related features of passengers. Meanwhile, target pre-location is used by combining the mixture of Gaussian (MoG) model and background subtraction, which can greatly reduce the following detection time. To address the tracking drift problem, inspired by the movement of ants in nature, we attempt to exploit the trajectory information to build a biologically inspired pheromone map and a 3D peak confidence map. Then, the number of passengers can be obtained by counting the regions of interest (ROI). Experimental results on an actual public bus transportation dataset show that this method outperforms some existing methods.