TY - GEN
T1 - Activity Recognition Based on DCNN and Kinect RGB Images
AU - Tan, Tan Hsu
AU - Gochoo, Munkhjargal
AU - Chen, Hong Syuan
AU - Liu, Shing Hong
AU - Huang, Yung Fa
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/4
Y1 - 2020/11/4
N2 - Recognizing daily activities of elderly people can detect abnormal activity of elder people living alone. Therefore, activity recognition has received much attention in recent years. This study aims to recognize daily activities of people by employing RGB activity images and deep convolutional neural network (DCNN). In this study, the Cornell Activity Dataset (CAD-60) collected by RGB-D camera via the Microsoft Kinect is employed to train the DCNN model. Then, the DCNN model is used to classify activity images. Experimental results of 4-fold cross validation show that the precision, recall, specificity, accuracy, and F1-score of 95.5%, 95.6%, 99.8%, and 99.6%, and 95.3%, respectively, are achieved. The result is superior to other existing systems, indicating the application potential of our work.
AB - Recognizing daily activities of elderly people can detect abnormal activity of elder people living alone. Therefore, activity recognition has received much attention in recent years. This study aims to recognize daily activities of people by employing RGB activity images and deep convolutional neural network (DCNN). In this study, the Cornell Activity Dataset (CAD-60) collected by RGB-D camera via the Microsoft Kinect is employed to train the DCNN model. Then, the DCNN model is used to classify activity images. Experimental results of 4-fold cross validation show that the precision, recall, specificity, accuracy, and F1-score of 95.5%, 95.6%, 99.8%, and 99.6%, and 95.3%, respectively, are achieved. The result is superior to other existing systems, indicating the application potential of our work.
KW - Activity Recognition
KW - CAD-60
KW - Deep Convolutional Neural Network (DCNN)
KW - RGB activity image
UR - http://www.scopus.com/inward/record.url?scp=85099702020&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099702020&partnerID=8YFLogxK
U2 - 10.1109/iFUZZY50310.2020.9297815
DO - 10.1109/iFUZZY50310.2020.9297815
M3 - Conference contribution
AN - SCOPUS:85099702020
T3 - 2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020
BT - 2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020
Y2 - 4 November 2020 through 7 November 2020
ER -