Affiliations: Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China
Correspondence:
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Corresponding author. Cai Linqin, Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, 400065 Chongqing, China. Tel.: +86 15215164090; Fax: +86 023 62461535; E-mail: [email protected].
Abstract: Hand gesture recognition is widely used in human-computer interaction (HCI) and has attracted substantial researching attentions. This paper aims to develop low-complexity and real-time solutions of dynamic hand gestures recognition using RGB-D depth sensor for natural human-computer interaction applications. We combine Euclidean distance between hand joints and shoulder center joint with the modulus ratios of skeleton features to generate a unifying feature descriptor for each dynamic hand gesture. And then, an improved dynamic time warping (IDTW) algorithm is proposed to obtain the final recognition results, which applies the weighted distance and a restricted search path to avoid the huge computation in conventional DTW and improves the recognition performance. Experimental results show that the proposed algorithm of dynamic hand gesture recognition not only achieves higher average recognition rate of 96.5% and better performance in response time, but also is robust to uncontrolled environments. Finally, according to our hand gesture recognition solutions, we develop one real-life HCI applications to control a virtual coalmine environment, which operates accurately and efficiently.
Keywords: Dynamic gesture, red green blue-depth (RGB-D), human-computer interaction (HCI), dynamic time warping (DTW), virtual environment
