TY - GEN
T1 - SlowFast-GCN
T2 - 1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
AU - Lin, Cheng Hung
AU - Chou, Po Yung
AU - Lin, Cheng Hsien
AU - Tsai, Min Yen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Human action recognition plays an important role in video surveillance, human-computer interaction, video understanding, and virtual reality. Different from two-dimensional object recognition, human action recognition is a dynamic object recognition with a time series relationship, and it faces many challenges from complex environments, such as color shift, light and shadow changes, and sampling angles. In order to improve the accuracy of human action recognition, many studies have proposed skeleton-based action recognition methods that are not affected by the background, but the current framework does not have much discussion on the integration of the time dimension.In this paper, we propose a novel SlowFast-GCN framework which combines the advantages of ST-GCN and SlowFastNet with dynamic human skeleton to improve the accuracy of human action recognition. The proposed framework uses two streams, one stream captures fine-grained motion changes, and the other stream captures static semantics. Through these two streams, we can merge the human skeleton features from two different time dimensions. Experimental results show that the proposed framework outperforms to state-of-the-art approaches on the NTU-RGBD dataset.
AB - Human action recognition plays an important role in video surveillance, human-computer interaction, video understanding, and virtual reality. Different from two-dimensional object recognition, human action recognition is a dynamic object recognition with a time series relationship, and it faces many challenges from complex environments, such as color shift, light and shadow changes, and sampling angles. In order to improve the accuracy of human action recognition, many studies have proposed skeleton-based action recognition methods that are not affected by the background, but the current framework does not have much discussion on the integration of the time dimension.In this paper, we propose a novel SlowFast-GCN framework which combines the advantages of ST-GCN and SlowFastNet with dynamic human skeleton to improve the accuracy of human action recognition. The proposed framework uses two streams, one stream captures fine-grained motion changes, and the other stream captures static semantics. Through these two streams, we can merge the human skeleton features from two different time dimensions. Experimental results show that the proposed framework outperforms to state-of-the-art approaches on the NTU-RGBD dataset.
KW - Graph Convolution Neural Network
KW - Skeleton base action recognition
KW - Temporal fusion
UR - http://www.scopus.com/inward/record.url?scp=85100050871&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100050871&partnerID=8YFLogxK
U2 - 10.1109/ICPAI51961.2020.00039
DO - 10.1109/ICPAI51961.2020.00039
M3 - Conference contribution
AN - SCOPUS:85100050871
T3 - Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
SP - 170
EP - 174
BT - Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 December 2020 through 5 December 2020
ER -