[Presentation] Long-Range Hand Gesture Recognition via Attention-based SSD Network ICRA 2021 

Authors: Liguang Zhou, Chenping Du, Zhenglong Sun, Tin Lun Lam, Yangsheng Xu
Corresponding author: Tin Lun Lam (Email: tllam@cuhk.edu.cn; Website: sites.google.c... ​)
Published in: 2021 IEEE International Conference on Robotics and Automation (ICRA), Xian, China, May 30 - June 5, 2021.
Visit Robotics & AI Lab CUHKSZ: rail.cuhk.edu.cn​
Title:
Long-Range Hand Gesture Recognition via Attention-based SSD Network
Abstract:
Hand gesture recognition plays an essential role in the human-robot interaction (HRI) field. Most previous research only studies hand gesture recognition in a short distance, which cannot be applied for interaction with mobile robots like unmanned aerial vehicles (UAVs) at a longer and safer distance. Therefore, we investigate the challenging long-range hand gesture recognition problem for the interaction between humans and UAVs. To this end, we propose a novel attention-based single shot multi-box detector (SSD) model that incorporates both spatial and channel attention for hand gesture recognition. We notably extend the recognition distance from 1 meter to 7 meters through the proposed model without sacrificing speed. Besides, we present a long-range hand gesture (LRHG) dataset collected by the USB camera mounted on mobile robots. The hand gestures are collected at discrete distance levels from 1 meter to 7 meters, where most of the hand gestures are small and at low resolution. Experiments with the self-built LRHG dataset show our methods reach the surprising performance-boosting over the state-of-the-art method like the SSD network on both short-range (1 meter) and long-range (up to 7 meters) hand gesture recognition tasks.