An Efficient Bus Crowdedness Classification System

We propose an efficient bus crowdedness classification system that can be used in daily life. In particular, we analyze and study the data collected from real bus, aiming to deal with the difficulty of bus congestion classification. Besides, we combine deep learning and computer vision technology to extract images or videos from the internal surveillance cameras of the bus. The information of crowd will finally be integrated with algorithms into a complete classification system. As a consequence, when the user enters the system and submits the image or video to be detected, the system will display the classification results in turn. The classification results include passenger density distribution, number of passengers, date, and algorithm running time. In addition, the user can use the mouse to delineate an area in the passenger density distribution map and count any image area.