Detecting occluded people for robotic guidance

Often overlooked in human-robot interaction is the challenge of people detection. For natural interaction, a robot must detect people without waiting for them to face the camera, get far enough away to be fully present, or center themselves fully within the field of view. Furthermore, it must happen without requiring immense amounts of processing that are not practical for real systems. In this work we focus on person detection in a guidance scenario, where occlusion is particularly prevalent. Using a layered approach with depth images, we can substantially improve detection rates under high levels of occlusion, and enable a robot to detect a target that is moving into and out of the field of view.