Multicamera edge-computing system for persons indoor location and tracking
Ángel Carro-Lagoa, Valentín Barral, M. González-López, Carlos J. Escudero, Luis Castedo
Abstract
This paper presents an indoor person localization and tracking system that uses multiple smart cameras equipped with artificial intelligence (AI) accelerators serving as edge-computing nodes. Our main contributions are as follows: (a) the development of a new multicamera tracking system for indoor scenarios; (b) the release of a multitarget multicamera tracking dataset; and (c) the development of an annotation mechanism based on waypoints. The system can simultaneously track several individuals while preserving their privacy and anonymity, because no images or sensitive data are transmitted outside the edge nodes. Only the position and appearance of each person were transmitted to the central server. In addition, a multitarget multicamera tracking dataset was released. The dataset contains recordings from five cameras in an indoor scenario and is annotated with the real-world coordinates of individuals. Ground-truth annotations were semiautomatically generated using a mechanism in which people equipped with mobile phones followed specific paths with predefined waypoints. Software related to the ground-truth annotation mechanism has also been released as open source.