People tracking in a smart campus context using multiple cameras - Matos, Henrique (2023)
Object multi-tracking has been a relevant topic for different applications, such as surveillance, mobility, and ambient intelligence. It is particularly challenging when considering open spaces, like Smart Cities, which demand multi-camera solutions with issues like re-identification. In this paper, we describe a framework aiming to provide multi-tracking of people throughout a university campus as part of a larger project (Lab4USpaces) to develop a Smart Campus initiative. Several object detection models and real-time tracking open-source algorithms were compared. The project contemplates a set of low-cost video cameras covering most of the campus, with or without overlapping. After researching different alternatives, the proposed framework uses the YOLOv7 tiny model for object detection, BoT-Sort for multiple object tracking, and Deep Person Reid for re-identification. We also faced challenges concerning the privacy and security of campus users. The multi-tracking system complies with current regulations since no personal identification is ever performed, and no images are stored for longer than necessary for object detection and re-identification. Besides describing the first prototype, this paper discusses some validation tests and describes some potential uses.