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Sungock Lee
PaiChai University
Computer Science & IT Electrical & Electronics Engineering

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Human activity recognition by using convolutional neural network Hankil Kim; Sungock Lee; Hoekyung Jung
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (463.908 KB) | DOI: 10.11591/ijece.v9i6.pp5270-5276

Abstract

In recent years, many researchers have studied the HAR (Human Activity Recognition) system. HAR using smart home sensor is based on computing in smart environment, and intelligent surveillance system conducts intensive research on peripheral support life. The previous system studied in some of the activities is a fixed motion and the methodology is less accurate. In this paper, vision-based studies using thermal imaging cameras improve the accuracy of motion recognition in intelligent surveillance systems. We use one of the deep learning architectures widely used in image recognition systems called Convolutional Neural Networks (CNN). Therefore, we use CNN and thermal cameras to provide accuracy and many features through the proposed method.
Co-Authors Hankil Kim Hoekyung Jung