Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Continuous hand gesture recognition based on trajectory shape information

Authors
Yang, CheoljongHan, David K.Ko, Hanseok
Issue Date
1-11월-2017
Publisher
ELSEVIER
Keywords
Gesture recognition; Human robot interaction; Convolution neural network; Conditional random fields; Trajectory segmentation; Trajectory shape modeling
Citation
PATTERN RECOGNITION LETTERS, v.99, pp.39 - 47
Indexed
SCIE
SCOPUS
Journal Title
PATTERN RECOGNITION LETTERS
Volume
99
Start Page
39
End Page
47
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/81592
DOI
10.1016/j.patrec.2017.05.016
ISSN
0167-8655
Abstract
In this paper, we propose a continuous hand gesture recognition method based on trajectory shape information. A key issue in recognizing continuous gestures is that performance of conventional recognition algorithms may be lowered by such factors as, unknown start and end points of a gesture or variations in gesture duration. These issues become particularly difficult for those methods that rely on temporal information. To alleviate the issues of continuous gesture recognition, we propose a framework that simultaneously performs both segmentation and recognition. Each component of the framework applies shape-based information to ensure robust performance for gestures with large temporal variation. A gesture trajectory is divided by a set of key frames by thresholding its tangential angular change. Variablesized trajectory segments are then generated using the selected key frames. For recognition, these trajectory segments are examined to determine whether the segment belongs to a class among intended gestures or a non-gesture class based on fusion of shape information and temporal features. In order to assess performance, the proposed algorithm was evaluated with a database of digit hand gestures. The experimental results indicate that the proposed algorithm has a high recognition rate while maintaining its performance in the presence of continuous gestures. (C) 2017 Elsevier B.V. All rights reserved.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > School of Electrical Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Ko, Han seok photo

Ko, Han seok
공과대학 (전기전자공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE