Robust real-time hand detection and localization for space human-robot interaction based on deep learning

Qing Gao, Jinguo Liu, Zhaojie Ju

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Abstract

Hand gestures are quite suitable for space human-robot interaction (SHRI) because of their natural and convenient features. While the detection and localization of hands are the premise and foundation for SHRI based on hand gestures. But hand gestures are very complicated and hand sizes are very small in some images. These problems make the robust real-time hand detection and local- ization very difficult. In this paper, a feature-map-fused single shot multibox detector (FF-SSD) which is a deep learning network is designed to deal with the problems of hand detection and localization in SHRI. First, the background of the method is introduced in this paper, including an astronaut assistant robot platform, the difficulties of hand detection and localization, and introduction of the state-of-the-art deep learning networks for object detection and localiza- tion. Then, the FF-SSD is proposed for detecting and localizing hands especially pony-size hands. This network magentatakes into consideration both accuracy and speed with balanced performance. And in the experiment part, the FF-SSD is trained and tested on hand databases which include a homemade database and two public databases. At last, the superiority of the proposed method is demonstrated compared with the state-of-the-art methods.
Original languageEnglish
Number of pages9
JournalNeurocomputing
Early online date24 Oct 2019
DOIs
Publication statusEarly online - 24 Oct 2019

Keywords

  • astronaut assistant robot
  • deep learning
  • hand detection and localization
  • SSD

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