Accurate Positioning via Cross-Modality Training

In this paper we propose a novel algorithm for tracking people in highly dynamic industrial settings, such as construction sites. We observed both short term and long term changes in the environment; people were allowed to walk in different parts of the site on different days, the field of view of fixed cameras changed over time with the addition of walls, whereas radio and magnetic maps proved unstable with the movement of large structures. To make things worse, the uniforms and helmets that people wear for safety make them very hard to distinguish visually, necessitating the use of additional sensor modalities. In order to address these challenges, we designed a positioning system that uses both anonymous and id-linked sensor measurements and explores the use of cross-modality training to deal with environment dynamics. The system is evaluated in a real construction site and is shown to outperform state of the art multi-target tracking algorithms designed to operate in relatively stable environments.