Hidden-Markov-Models-based dynamic hand gesture recognition
type: Item
instance of: scholarly article
title: Hidden-Markov-Models-based dynamic hand gesture recognition
zbMATH Open document ID: 1264.68155
DOI: 10.1155/2012/986134
author: Ming Xia
author: Huiwen Cai
author: Yong Gao
author: Xiao-Yan Wang
author: Carlo Cattani
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: This paper is concerned with the recognition of dynamic hand gestures. A method based on Hidden Markov Models (HMMs) is presented for dynamic gesture trajectory modeling and recognition. Adaboost algorithm is used to detect the user's hand and a contour-based hand tracker is formed combining condensation and partitioned sampling. Cubic B-spline is adopted to approximately fit the trajectory points into a curve. Invariant curve moments as global features and orientation as local features are computed to represent the trajectory of hand gesture. The proposed method can achieve automatic hand gesture online recognition and can successfully reject atypical gestures. The experimental results show that the proposed algorithm can reach better recognition results than the traditional hand recognition method.
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 94A08
zbMATH DE Number: 6173747
Wikidata QID: Q58912805
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/986134
OpenAlex ID: W2160395934
cites work: A probabilistic exclusion principle for tracking multiple objects
cites work: Improved generalized belief propagation for vision processing
description: scientific article; zbMATH DE number 6173747