| 摘 要: 针对ResNeXt网络(残差网络)中存在的对特征提取不充分，以及数据集中背景信息干扰的问题，将ResNeXt网络和注意力机制相结合，提出了一种基于注意力机制的ResNeXt模型。首先，在ResNeXt网络的基础上，将浅层和深层的特征融合生成新型网络结构。其次，将全连接层由全局平均池化层替代，然后在通道空间注意力机制中添加一个条件因子，同时将改进后的注意力机制嵌入上述网络中。最后，在UCF101和HMDB51上分别进行实验，得到了95.2%和65.6%的准确率。研究表明，本文提出的模型可以有效地提取关键特征，充分利用不同层次的特征信息获得较好的准确率。 |
| 关键词: 人体行为识别；注意力机制；ResNeXt；全局平均池化 |
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中图分类号: TP183
文献标识码: A
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| Human Action Recognition Method based on Attention Mechanism and Improved ResNeXt Network |
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WANG Haofei, LI Junfeng
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(Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China)
haofeiwang@yeah.net; ljf2003zz@163.com
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| Abstract: Aiming at problems of insufficient feature extraction in ResNeXt network and background information interference in the dataset, this paper proposes a ResNeXt model based on attention mechanism, which combines the ResNeXt network and attention mechanism. First, based on ResNeXt network, shallow and deep features are merged to generate a new network structure. Second, the fully connected layer is replaced by a global average pooling layer. Then channel attention mechanism is improved by adding a condition factor. At the same time, the improved attention mechanism is embedded in the above-mentioned network. Finally, experiments are performed on UCF101 and HMDB51 respectively, and the accuracy rates of 95.2% and 65.6% are obtained. Experiments show that the proposed model can effectively extract key features, and make full use of feature information of different layers to achieve better accuracy. |
| Keywords: human action recognition; attention mechanism; ResNeXt network; global average pooling |
