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Rule-based trajectory segmentation for modeling hand motion trajectory

Authors
Beh, JounghoonHan, DavidKo, Hanseok
Issue Date
4월-2014
Publisher
ELSEVIER SCI LTD
Keywords
Trajectory segmentation; Hand gesture recognition; Hidden Markov model; HMM initialization
Citation
PATTERN RECOGNITION, v.47, no.4, pp.1586 - 1601
Indexed
SCIE
SCOPUS
Journal Title
PATTERN RECOGNITION
Volume
47
Number
4
Start Page
1586
End Page
1601
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/98936
DOI
10.1016/j.patcog.2013.11.010
ISSN
0031-3203
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. (C) 2013 Elsevier Ltd. All rights reserved.
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공과대학 (전기전자공학부)
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