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학술지 Posture Recognition Using Ensemble Deep Models under Various Home Environments
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변영현, 이재연, 김도형, 곽근창
Applied Sciences, v.10 no.4, pp.1213-28
20HS2500, 고령 사회에 대응하기 위한 실환경 휴먼케어 로봇 기술 개발, 이재연
This paper is concerned with posture recognition using ensemble convolutional neural networks (CNNs) in home environments. With the increasing number of elderly people living alone at home, posture recognition is very important for helping elderly people cope with sudden danger. Traditionally, to recognize posture, it was necessary to obtain the coordinates of the body points, depth, frame information of video, and so on. In conventional machine learning, there is a limitation in recognizing posture directly using only an image. However, with advancements in the latest deep learning, it is possible to achieve good performance in posture recognition using only an image. Thus, we performed experiments based on VGGNet, ResNet, DenseNet, InceptionResNet, and Xception as pre-trained CNNs using five types of preprocessing. On the basis of these deep learning methods, we finally present the ensemble deep model combined by majority and average methods. The experiments were performed by a posture database constructed at the Electronics and Telecommunications Research Institute (ETRI), Korea. This database consists of 51,000 images with 10 postures from 51 home environments. The experimental results reveal that the ensemble system by InceptionResNetV2s with five types of preprocessing shows good performance in comparison to other combination methods and the pre-trained CNN itself.
Convolutional neural network, Ensemble deep models, Home environments, Posture database, Posture recognition, Preconfigured CNNs
KSP 제안 키워드
Combination method, Convolution neural network(CNN), Deep model, Elderly People, Ensemble System, Learning methods, Research institute, deep learning(DL), machine Learning, posture recognition, the body
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