Deep Learning of Human Posture Image Classification using Convolutional Neural Networks
No Thumbnail Available
Authors
Rababaah, Aaron
Issue Date
2022-08-08
Type
Peer Reviewed
Journal Article
Journal Article
Language
Keywords
Alternative Title
Abstract
In this paper a study of deep learning applied to human posture image classification using convolutional neural networks (CNNs) is presented. Typical computer vision workflow includes in the early stages: data conditioning, feature extraction, dimensionality reduction/feature selection whereas, in CNNs, these stages are eliminated which provides a big advantage of automatic feature extraction. In this work, CNNs are applied to human posture classification. Collected human postures included standing with five different variations, sitting with two different variations, bending and sleeping with two different variations. More than 6000 samples were collected for training and validation. Several independent experiments were conducted each of which has a different number of filters/kernels ranging within [1, 32]. The results of the experimental work showed that number of features influenced the classification accuracy significantly as the lowest CNN model produced 91.76% and the highest model produced 98.57% classification accuracy.
Description
Citation
Rababaah, A. R. (2022). Deep learning of human posture image classification using convolutional neural networks. International Journal of Computing Science and Mathematics, 15(3), 273-288.
Publisher
International Journal of Computing Science and Mathematics