Visual and automatic bus passenger counting based on a deep tracking-by-detection system

In this paper, we address the industrial constraints of automatic passenger counting in city buses through a deep architecture able to deal with images taken from low cost 2D cameras placed above the doorstep, from a zenithal point of view. The challenge is then to handle highly variable scenes due to passengers appearance (hair color, hats, height), bus population density at rush hour and changes in scene illumination. The scientific breakthrough related to deep learning applied to computer vision as well as the system embedding requirements for this task motivate us to integrate in this context a lightweight convolutional multiobject tracker which was especially designed for embedded applications and performed well on the MOT Challenge. We here evaluate it in an industrial context on our large scale in-situ dataset, labelled for detection, multi-target tracking and counting, and present a complete and embedded counting system meeting the requirements of our application.