TY - GEN
T1 - Video action recognition method based on attention residual network and LSTM
AU - Zhang, Yu
AU - Dong, Pengyue
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - A video action recognition method based on attention residual network and long-term memory network(LSTM) is proposed, which is to solve the problems that the existing human action recognition methods are prone to overfitting, susceptible to interference information, and lack of feature expression ability. In the beginning, the traditional data preprocessing method and sampling method are improved to enhance the generalization ability of the model. Then, a residual network with attention is proposed to improve the feature extraction ability of the network. At length, LSTM is used to recognize video actions. Experimental results on UCF YouTube dataset show that the proposed method can recognize the actions in video more effectively than other similar methods in this field, and the recognition rate reaches 95.45%.
AB - A video action recognition method based on attention residual network and long-term memory network(LSTM) is proposed, which is to solve the problems that the existing human action recognition methods are prone to overfitting, susceptible to interference information, and lack of feature expression ability. In the beginning, the traditional data preprocessing method and sampling method are improved to enhance the generalization ability of the model. Then, a residual network with attention is proposed to improve the feature extraction ability of the network. At length, LSTM is used to recognize video actions. Experimental results on UCF YouTube dataset show that the proposed method can recognize the actions in video more effectively than other similar methods in this field, and the recognition rate reaches 95.45%.
KW - Action recognition
KW - Attention mechanism
KW - Long short-term memory network
KW - Residual network
UR - http://www.scopus.com/inward/record.url?scp=85125193576&partnerID=8YFLogxK
U2 - 10.1109/CCDC52312.2021.9601577
DO - 10.1109/CCDC52312.2021.9601577
M3 - Conference contribution
AN - SCOPUS:85125193576
T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
SP - 3611
EP - 3616
BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Chinese Control and Decision Conference, CCDC 2021
Y2 - 22 May 2021 through 24 May 2021
ER -