Understanding human-object interaction in RGB-D videos for human robot interaction

Abstract

Detecting small hand-held objects plays a critical role for human-robot interaction, because the hand-held objects often reveal the intention of the human, e.g., use a cell phone to make a call or use a cup to drink, thus helps the robots understand the human behavior and response accordingly. Existing solutions relying on wearable sensor to detect hand-held objects often comprise the user experiences thus may not be preferred. With the development of commodity RGB-D sensors, e.g., Microsoft Kinect II, RGB and depth information have been used for the understanding of human actions and recognizing objects. Motivated by the previous success, we propose to detect hand-held objects using RGB-D sensor. However, instead of performing object detection alone, we propose to leverage human body pose as the context to achieve robust hand-held object detection in RGB-D videos. Our system demonstrates a person can interact with a humanoid social robot with hand-held object such as a cell phone or a cup. Experimental evaluations validate the effectiveness of this proposed method.