残余物
计算机科学
卷积(计算机科学)
卷积神经网络
序列(生物学)
特征(语言学)
人工智能
人工神经网络
算法
模式识别(心理学)
数据挖掘
语言学
哲学
生物
遗传学
作者
Jianxin Li,Jie Liu,Chao Li,Fei Jiang,Jinyu Huang,Shanshan Ji,Yang Liu
标识
DOI:10.1080/17517575.2023.2180777
摘要
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.
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