Dual-hand detection for human-robot interaction by a parallel network based on hand detection and body pose estimation

Qing Gao, Jinguo Liu, Zhaojie Ju, Xin Zhang

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Abstract

In this study, a parallel network based on hand detection and body pose estimation is proposed to detect and distinguish human’s right and left hands. The network is employed to human-robot interaction (HRI) based on hand gestures. This method fully uses hand feature information and hand information in the human body structure. One channel in the network uses a ResNet-Inception-Single Shot MultiBox Detector to extract hand feature information for human’s hand detection. The other channel estimates human body pose first and then estimates the positions of the left and right hands using the forward kinematic tree of the human skeleton structure. Thereafter, the results of the two channels are fused. In the fusion module, the human body structure can be utilized to correct hand detection results and distinguish between the right and left hands. Experimental results verify that the parallel deep neural network can effectively improve the accuracy of hand detection and distinguish between the right and left hands effectively. This method is also used for the hand gesture-based interaction between astronauts and an astronaut assistant robot. Our method can be suitably used in this HRI system.
Original languageEnglish
Pages (from-to)9663-9672
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number12
Early online date15 Feb 2019
DOIs
Publication statusEarly online - 15 Feb 2019

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