TY - JOUR
T1 - Temporal action proposal for online driver action monitoring using Dilated Convolutional Temporal Prediction Network
AU - Wen, Boge
AU - Chen, Siyuan
AU - Shao, Chenhui
N1 - Publisher Copyright:
© 2020
PY - 2020/10
Y1 - 2020/10
N2 - This paper presents a new approach for temporal detection of short human activities in untrimmed videos. Most present methods for temporal action detection, to our best knowledge, are trained on public action datasets that feature actions spanning up to tens and hundreds of seconds. However, it is often desired in manufacturing, transportation, and other safety-critical scenes that fine-grained actions be automatically detected, classified, and monitored. We propose a new Dilated Convolutional Temporal Prediction Network that features 1-D dilated convolution operation in a Residual network (ResNet)-like architecture for the generation of action proposals on orders of fractions of a second. The new architecture is used as a part of the action monitoring pipeline in subway cars. Experiments demonstrate that the proposed model outperforms the state-of-the-art on the task of temporal action proposal generation on a real-world video dataset, while achieving a fast processing speed suitable for online monitoring.
AB - This paper presents a new approach for temporal detection of short human activities in untrimmed videos. Most present methods for temporal action detection, to our best knowledge, are trained on public action datasets that feature actions spanning up to tens and hundreds of seconds. However, it is often desired in manufacturing, transportation, and other safety-critical scenes that fine-grained actions be automatically detected, classified, and monitored. We propose a new Dilated Convolutional Temporal Prediction Network that features 1-D dilated convolution operation in a Residual network (ResNet)-like architecture for the generation of action proposals on orders of fractions of a second. The new architecture is used as a part of the action monitoring pipeline in subway cars. Experiments demonstrate that the proposed model outperforms the state-of-the-art on the task of temporal action proposal generation on a real-world video dataset, while achieving a fast processing speed suitable for online monitoring.
KW - Computer vision
KW - Convolutional neural network
KW - Online action monitoring
KW - Temporal action proposal
UR - http://www.scopus.com/inward/record.url?scp=85086498336&partnerID=8YFLogxK
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U2 - 10.1016/j.compind.2020.103255
DO - 10.1016/j.compind.2020.103255
M3 - Article
AN - SCOPUS:85086498336
SN - 0166-3615
VL - 121
JO - Computers in Industry
JF - Computers in Industry
M1 - 103255
ER -