Human Pose Estimation in Presence of Occlusion Using Depth Camera Sensors, in Human-Robot Coexistence Scenarios
Andrea Casalino, Sebastian Guzman, Andrea Maria Zanchettin, Paolo Rocco · 2018
Collaborative robotics over the last few years has gained increasing interest in the industrial scenario. Co-bots can be equipped with vision sensors and cognitive software layers, allowing the robot to figure out human intentions. To make this level of perception possible, human pose estimation algorithms are required. Several techniques have been already proposed to tackle this problem, which however present some weaknesses in particular when occlusions occur. This work proposes an algorithm for human pose estimation in the situations of partial occlusion, based on particle filter techniques. We have proved its validity in a realistic human-robot coexistence scenario, where a human and a dual arm robot have to perform tasks in a shared workspace.