Rule-based trajectory segmentation for modeling hand motion trajectory

Jounghoon Beh, David Han, Hanseok Ko

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

In this paper, we propose a simple but effective method of modeling hand gestures based on the angles and angular change rates of the hand trajectories. Each hand motion trajectory is composed of a unique series of straight and curved segments. In our Hidden Markov Model (HMM) implementation, these trajectories are modeled as a connected series of states analogous to the series of phonemes in speech recognition. The novelty of the work presented herein is that it provides an automated process of segmenting gesture trajectories based on a simple set of threshold values in the angular change measure. In order to represent the angular distribution of each separated state, the von Mises distribution is used. A likelihood based state segmentation was implemented in addition to the threshold based method to ensure that the gesture sets are segmented consistently. The proposed method can separate each angular state of the training data at the initialization step, thus providing a solution to mitigate the ambiguities on initializing the HMM. The effectiveness of the proposed method was demonstrated by the higher recognition rates in the experiments compared to the conventional methods.

Original languageEnglish
Pages (from-to)1586-1601
Number of pages16
JournalPattern Recognition
Volume47
Issue number4
DOIs
Publication statusPublished - 2014 Apr

Keywords

  • HMM initialization
  • Hand gesture recognition
  • Hidden Markov model
  • Trajectory segmentation

ASJC Scopus subject areas

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

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