Recognition of Dynamic Hand Gesture using Hidden Markov Model

被引：0

作者：

Lynn, Kok Yi ^{[1
]}

Wong, Farrah ^{[1
]}

机构：

[1] Univ Malaysia Sabah, Fac Engn, Kota Kinabalu, Sabah, Malaysia

来源：

2022 INTERNATIONAL CONFERENCE ON GREEN ENERGY, COMPUTING AND SUSTAINABLE TECHNOLOGY (GECOST) | 2022年

关键词：

Hand Gesture; Hand Gesture Path; Hidden Markov Model;

D O I：

10.1109/GECOST55694.2022.10010517

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A simple methodology for Malaysian Sign Language recognition using image processing is developed. Frame difference, thresholding of the frame difference and edge detection are used for the pre-processing stage. For the segmentation part, HSV color detection is used to detect the skin color. Combination of threshold of the frame difference, edge detection and HSV color detection are used to segment out the hand region. Feature extraction is used to track the hand gesture path. Centroid of the hand motion and 16-directional codewords are used. For classification, Hidden Markov Models (HMM) is used to recognize the hand gesture. A total of 100 videos are used for training and testing purpose. Overall, there are 14 gestures of the Malaysia Sign Language that were used in the recognition. The percentage of recognition for the testing set is 92.86%.

引用

页码：419 / 422

页数：4

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