To facilitate full-loaded commandos to control reconnaissance robots, in this paper, we propose a wearable hand posture control system based on egocentric-vision by imitating the sign language interaction way among commandos. Considering the characteristics of the egocentric-vision on the battlefield, such as complicated backgrounds, large ego-motions and extreme transitions in lighting, a new hand detector based on Binary Edge HOG Block (BEHB) features is proposed to extract articulated postures from the egocentric-vision. Different from many other methods that use skin color cues, our proposed hand detector adopts contour cues and part-based voting idea. This means that our algorithm can be used on the battlefield even in dark environment, because infrared cameras can be used to get contour images rather than skin color images. The experiment result shows that the proposed hand detector can get a better posture detection result on the NUS hand posture dataset II. To improve hand recognition accuracy, a deep ensemble hybrid classifier is proposed by combing hybrid CNN-SVM classifier and ensemble technique. Compared with other state-of-art algorithms, the proposed classifier yields a recognition accuracy of 97.72 % on the NUS hand posture dataset II. At last, to reduce misjudgments during consecutive posture switches, a vote filter is proposed and applied to the sequence of the recognition results. The scout experiment shows that our wearable hand posture control system is more suitable than traditional hand-held controllers for full-loaded commandos to control reconnaissance robots.
Journal of Intelligent & Robotic Systems – Springer Journals
Published: Nov 15, 2016
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera