RGB-D human detection and tracking for industrial environments

Abstract

Reliably detecting and tracking movements of nearby workers on the factory floor are crucial to the safety of advanced manufacturing automation in which humans and robots share the same workspace. In this work, we address the problem of multiple people detection and tracking in industrial environments by proposing algorithms which exploit both color and depth data to robustly track people in real time. For people detection, a cascade organization of these algorithms is proposed, while tracking is performed based on a particle filter which can interpolate sparse detection results by exploiting color histograms of people. Tracking results of different combinations of the proposed methods are evaluated on a novel dataset collected with a consumer RGB-D sensor in an industrial-like environment. Our techniques obtain good tracking performances even in an industrial setting and reach more than 30Hz update rate. All these algorithms have been released as open source as part of the ROS-Industrial project.