Use of Deep Learning Methods for People Counting in Public Transport
Daniel D. Baumann, Martin S. Sommer, Yannick Schrempp, Eric Sax · 2022
This paper uses deep learning in the field of public transport, where it is of immense interest to know the occupation of vehicles for their coordination and scheduling. Counting people on a bus or train is very often still done manually, whereas this paper presents a modern approach with just the use of camera images and deep learning. The people counter presented in this work consists of two parts, in each of which a neural network has been optimized. In the first step, persons are recognized by means of a person detector while this information is used in the second step for counting people. For the detection of people at the bus entrance, RetinaNet was selected as the model and optimized. The output of the person detector was then used to optimize a novel architecture of a neural network. This allows determining the number of people getting on and off a bus in a video.