Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition.
Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition.
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DOI：
10.1155/2022/4816549
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发表时间：
2022
影响因子：
--
通讯作者：
Sun, Gengxin
中科院分区：
文献类型：
--
作者：
Zhu, Maochang;Bin, Sheng;Sun, Gengxin
三维卷积网络（3DCNN）是运动识别研究的重要领域。本文的研究工作对传统的三维卷积网络进行了优化，引入了自注意机制，提出了一种新的网络模型来分析和处理复杂的人体运动视频。在本研究中，平均跳帧采样和缩放和one-hot编码用于数据预处理，以保留更多的功能，在有限的数据。实验结果表明，本文创新性地设计了一种结合注意力机制框架的轻量级三维卷积网络，模型的参数数量减少了90%以上，仅为170万左右。本研究比较了不同模型在不同分类中的性能，发现本研究提出的模型在复杂的人体运动视频分类中表现良好。其识别率比C3 D模型提高了1%-8%。
Three-dimensional convolutional network (3DCNN) is an essential field of motion recognition research. The research work of this paper optimizes the traditional three-dimensional convolution network, introduces the self-attention mechanism, and proposes a new network model to analyze and process complex human motion videos. In this study, the average frame skipping sampling and scaling and the one-hot encoding are used for data pre-processing to retain more features in the limited data. The experimental results show that this paper innovatively designs a lightweight three-dimensional convolutional network combined with an attention mechanism framework, and the number of parameters of the model is reduced by more than 90% to only about 1.7 million. This study compared the performance of different models in different classifications and found that the model proposed in this study performed well in complex human motion video classification. Its recognition rate increased by 1%–8% compared with the C3D model.
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