Towards robust ego-centric hand gesture analysis for robot control

Wearable device with an ego-centric camera would be the next generation device for human-computer interaction such as robot control. Hand gesture is a natural way of ego-centric human-computer interaction. In this paper, we present an ego-centric multi-stage hand gesture analysis pipeline for robot control which works robustly in the unconstrained environment with varying luminance. In particular, we first propose an adaptive color and contour based hand segmentation method to segment hand region from the ego-centric viewpoint. We then propose a convex U-shaped curve detection algorithm to precisely detect positions of fingertips. And parallelly, we utilize the convolutional neural networks to recognize hand gestures. Based on these techniques, we combine most information of hand to control the robot and develop a hand gesture analysis system on an iPhone and a robot arm platform to validate its effectiveness. Experimental result demonstrates that our method works perfectly on controlling the robot arm by hand gesture in real time.