Journal article
Continuous hand gesture recognition based on trajectory shape information
Pattern recognition letters, v 99, pp 39-47
01 Nov 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
•Simultaneous recognition and segmentation of continuous hand gesture trajectories based on trajectory shape information.•Generating variable sized candidates for trajectory segments using shape-based key frame extraction.•Fusion of trajectory shape recognition and temporal feature recognition to stream gesture input.
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. Variable-sized 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.
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Details
- Title
- Continuous hand gesture recognition based on trajectory shape information
- Creators
- Cheoljong Yang - Korea UniversityDavid K. Han - Office of Naval ResearchHanseok Ko - Korea University
- Publication Details
- Pattern recognition letters, v 99, pp 39-47
- Publisher
- Elsevier
- Number of pages
- 9
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000413463700006
- Scopus ID
- 2-s2.0-85019950586
- Other Identifier
- 991021931083104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence