文章基本信息

标题：A New Method for Human Posture Recognition Using Principal Component Analysis and Artificial Neural Network
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作者：M. Z. Uddin ; M. A. Yousuf
期刊名称：Journal of Scientific Research
印刷版ISSN：2070-0237
电子版ISSN：2070-0245
出版年度：2015
卷号：7
期号：3
页码：11-19
DOI：10.3329/jsr.v7i3.19527
语种：English
出版社：Rajshahi University
摘要：The recognition of human posture from images is currently a very active area of research in computer vision. This paper presents a novel recognition method to determine a human posture is of walking or sitting using Principal Component Analysis (PCA) and Artificial Neural Network (ANN). In this paper, two types of learning are used to recognize the human posture. One is unsupervised and another is supervised learning. We have used PCA for unsupervised learning and ANN for supervised learning. To evaluate the performance of the proposed method, we have considered four types of human posture; walking, sitting, right leg up-down and left leg up-down. The experimental results on the human action of walking, sitting, right leg up-down and left leg up-down database show that our approach produces accurate recognition.
其他摘要：The recognition of human posture from images is currently a very active area of research in computer vision. This paper presents a novel recognition method to determine a human posture is of walking or sitting using Principal Component Analysis (PCA) and Artificial Neural Network (ANN). In this paper, two types of learning are used to recognize the human posture. One is unsupervised and another is supervised learning. We have used PCA for unsupervised learning and ANN for supervised learning. To evaluate the performance of the proposed method, we have considered four types of human posture; walking, sitting, right leg up-down and left leg up-down. The experimental results on the human action of walking, sitting, right leg up-down and left leg up-down database show that our approach produces accurate recognition.
关键词：Human action recognition;Principal Component Analysis (PCA);Feature extraction;Artificial Neural Network (ANN).