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A hyperautomative human behaviour recognition algorithm based on improved residual network
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DOI：10.1080/17517575.2023.2180777.png)
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When dealing with the mutual storage relationship of behavioral features in long time sequence video, the convolutional neural network is easy to miss important feature information. To solve the above problems, this paper proposes a super automatic algorithm combining nonlocal convolution and three-dimensional convolution neural network. The algorithm uses sparse sampling to segment the long time sequence video to reduce the amount of redundant information, and integrates non-local convolution into the residual neural network, thus forming a super automatic full variational - L1 algorithm. Experimental results show that the proposed method can significantly improve the efficiency of behavior recognition.
Keyword:
Hyperautomation
behaviour recognition
residual network
convolutional neural network
deep learning
nonlocal convolution
期刊
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3.9
论文数:
2.8K
被引数:
1.8K
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