Open AccessHuman Activity Recognition Based on Residual Network
Author(s) -
Jing-Xuan Zhang,
Shaojie Qiao,
Zhiyu Lin,
Yang Zhou
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/693/1/012041
Subject(s) - computer science , residual neural network , convolutional neural network , residual , activity recognition , artificial intelligence , artificial neural network , pattern recognition (psychology) , deep learning , machine learning , limiting , feature (linguistics) , engineering , algorithm , mechanical engineering , linguistics , philosophy
With the development of information technology and the blooming of artificial intelligence, human activity recognition (HAR) has become a hot research topic. HAR has a certain prospect and practical value for mobile medicine and security monitoring, which has attracted the attention of many researchers. However, most of the existing HAR methods rely on the shallow feature learning mechanism, and the features learned by these methods are not completely consistent with the actual activities. To solve this problem, a human motion recognition method based on residual network (ResNet) is proposed. In addition, we modified the internal structure of ResNet by limiting the number of its network layers within a certain range to prevent over fitting and degradation. Our proposed method has better recognition performance than methods using pure convolutional neural networks(CNN), and it achieves 95.66% recognition accuracy.