Continuous hand gesture recognition based on trajectory shape information

Cheoljong Yang, David K. Han, Hanseok Ko

Research output: Contribution to journalArticle

18 Citations (Scopus)

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. 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.

Original languageEnglish
JournalPattern Recognition Letters
DOIs
Publication statusAccepted/In press - 2016 Oct 15

Keywords

  • Conditional random fields
  • Convolution neural network
  • Gesture recognition
  • Human robot interaction
  • Trajectory segmentation
  • Trajectory shape modeling

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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