Adaptive local spatiotemporal feature extraction based on RGB-D data - Details

Author：

Lin, J. (Lin, J..) | Ruan, X. (Ruan, X..) | Yu, N. (Yu, N..) | Ouattara, S. (Ouattara, S..) | Zhu, X. (Zhu, X..)

Indexed by：

Scopus PKU CSCD

Abstract：

Noise　and　global　empirical　motion　constraints　seriously　affect　extracting　accurate　and　sufficient　spatiotemporal　features　for　one-shot　learning　gesture　recognition.　To　tackle　the　problem,　an　adaptive　local　spatiotemporal　feature　extraction　approach　with　both　color　and　depth　(RGB-D)　information　fused　was　proposed.　Firstly,　pyramids　and　optical　flow　pyramids　of　successive　two　gray　frames　and　two　depth　frames　were　built　as　scale　space.　Then,　motion　regions　of　interest　(MRoIs)　were　adaptively　extracted　according　to　horizontal　and　vertical　variances　of　the　gray　and　depth　optical　flow.　Subsequently,　corners　were　just　detected　as　interest　points　in　the　MRoIs.　These　interest　points　were　selected　as　keypoints　only　if　their　optical　flow　meet　adaptive　local　gray　and　depth　motion　constraints.　The　local　motion　constraints　were　adaptively　determined　in　each　MRoI.　Finally,　SIFT-like　descriptors　were　calculated　in　improved　gradient　and　motion　spaces.　Experimental　results　of　ChaLearn　dataset　demonstrate　that　the　proposed　approach　has　higher　recognition　accuracy　that　is　comparable　to　the　published　approaches.　©　2016,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.

Keyword：

Adaptive; Gesture recognition; Motion regions of interest; One-shot learning; Spatiotemporal feature

Author Community：

[ 1 ] [Lin, J.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Lin, J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Univercity of Technology, Beijing, 100124, China
[ 3 ] [Ruan, X.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Ruan, X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Univercity of Technology, Beijing, 100124, China
[ 5 ] [Yu, N.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Yu, N.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Univercity of Technology, Beijing, 100124, China
[ 7 ] [Ouattara, S.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Ouattara, S.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Univercity of Technology, Beijing, 100124, China
[ 9 ] [Zhu, X.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Zhu, X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Univercity of Technology, Beijing, 100124, China

Reprint Author's Address：

Email：

Show more details

Related Keywords：

Source ：

Journal of Beijing University of Technology

ISSN： 0254-0037

Year： 2016

Issue： 11

Volume： 42

Page： 1643-1651

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 5

Affiliated Colleges：

信息科学技术学院本学院/部未明确归属的数据

Get Fulltext

DOI Library Discovery Baidu Scholar Search SCOPUS

Type
Departments

All Years Choose Year From to