An inexpensive monocular vision system for tracking humans in industrial environments

We report on a novel vision-based method for reliable human detection from vehicles operating in industrial environments in the vicinity of workers. By exploiting the fact that reflective vests represent a standard safety equipment on most industrial worksites, we use a single camera system and active IR illumination to detect humans by identifying the reflective vest markers. Adopting a sparse feature based approach, we classify vest markers against other reflective material and perform supervised learning of the object distance based on local image descriptors. The integration of the resulting per-feature 3D position estimates in a particle filter finally allows to perform human tracking in conditions ranging from broad daylight to complete darkness.