Comparative Analysis on Few Typical Algorithms of Gesture Recognition for Human-robot Interaction

Abstract

Human-robot interaction (HRI) is considered as one of the key techniques of space intelligence robots. Few typical features of complicated human actions need to be captured and understood accurately by intelligent robot to ensure free communication and interaction between both above in real-time, especially for identifying and tracking hand state. There are four approaches for gesture recognition, including algorithm based on Kinect V2 SDK, model-based particle swarm optimization algorithm (PSO), deep learning algorithm and gesture module based on Baidu AI platform. These have been selected to compare in the form of principle, calculating time, robustness, range, environmental adaptability and correct rate respectively. The comparative results have generalized that both the second and the third algorithm have better performance than other algorithms in the above aspects such as calculating efficiency, robustness, detecting range, external disturbance and correct ratio. Particularly, the second algorithm is not only suitable for close range, but also suitable for multi-view cases. However, the third algorithm can have better performance, but depends on precise network model and weights by introducing lots of positive and negative gesture samples.