Research on gesture recognition of smart data fusion features in the IoT
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SMART DATA AGGREGATION INSPIRED PARADIGM & APPROACHES IN IOT APPLNS
Research on gesture recognition of smart data fusion features in the IoT Chong Tan1 • Ying Sun1,2 • Gongfa Li1,2,4 • Guozhang Jiang2,3 • Disi Chen5 • Honghai Liu5 Received: 27 September 2018 / Accepted: 8 January 2019 Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract With the rapid development of Internet of things technology, the interaction between people and things has become increasingly frequent. Using simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information; in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed; and by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method. Keywords Gesture recognition Fusion features Smart data aggregation Hu moment SVM
1 Introduction With the development of Internet technology and communication technology, the Internet of things technology has gradually been developed and applied. The Internet of things technology mainly refers to the development of corresponding functions in the real world to transmit, & Gongfa Li [email protected] 1
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
2
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
3
Research Center of Biologic Manipulator and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
4
Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan 430081, China
5
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
process and execute smart data. This requires the Internet of things to have the corresponding computing power and perceptual power, so that these real-world data can be converted into smart data and ultimately achieve mutual interaction between people and things [1, 2]. The research background of this paper is the somatosensory interaction technology based on the overall environment of the Internet of things. At present, research on gesture recognition technology has become an important research direction in digital image processing, artificial intelligence,
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