[1]李建平,赖永倩.基于注意力机制和残差网络的视频行为识别[J].计算机技术与发展,2023,33(04):69-74.[doi:10. 3969 / j. issn. 1673-629X. 2023. 04. 010]
　LI Jian-ping,LAI Yong-qian.Video Behavior Recognition Based on Attention Mechanism and Residual Network[J].,2023,33(04):69-74.[doi:10. 3969 / j. issn. 1673-629X. 2023. 04. 010]
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基于注意力机制和残差网络的视频行为识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年04期
页码:
69-74
栏目:
媒体计算
出版日期:
2023-04-10

文章信息/Info

Title:
Video Behavior Recognition Based on Attention Mechanism and Residual Network
文章编号:
1673-629X(2023)04-0069-06
作者:
李建平; 赖永倩
东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318
Author(s):
LI Jian-ping; LAI Yong-qian
School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China
关键词:
深度学习; 残差网络; 三维卷积网络; 视频行为识别; 注意力机制
Keywords:
deep learning; residual network; three dimensional convolution network; video behavior recognition; attention mechanism
分类号:
TP391. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 04. 010
摘要:
针对现有的视频人体行为识别模型识别能力有限,以及双流识别方法易受光照因素的影响导致时间成本较高的问题,提出一种基于注意力机制的 ResNeXt 模型用于识别视频中的人体行为。 将经过预处理后的视频帧数据作为该模型的输入,该卷积网络模型使用 ResNeXt101 层网络作为核心残差块。 在 ResNeXt 三维卷积神经网络模型的基础上,通过引入注意力机制来加强重要的特征信道,提高网络模型的特性表示及稳健性。 使用 Kinetics 的预训练模型,对 UCF-101和HMDB-51数据进行了训练和学习,迭代 200 次后,在验证集上的识别率分别达到了 96.0% 和 69.9% 。 实验结果显示,该模型能有效识别视频中的时空特征,与以往的识别模型相比准确率有所提高,且在人体行为识别任务中识别率较好。该模型能在保证深层网络的同时,使特征不丢失并且防止发生过拟合,同时识别的正确率也得到了改善,证明了该模型是有效可行的。
Abstract:
In view of the limited recognition ability of the existing video human behavior recognition models and the high time cost of thedual stream recognition method due to the influence of lighting factors,we propose a ResNeXt model based on attention mechanism torecognize human behavior in video. The preprocessed video frame data is used as the input of the model, and the ResNeXt101 layernetwork is used as the core residual block by the convolution network model. On the basis of ResNeXt three-dimensional convolutionalneural network model, attention mechanism is introduced to strengthen important characteristic channels and improve the characteristic representation and robustness of the network model. We use the pre-training model of Kinetics dynamics to train and learn the data ofUCF-101 and HMDB-51. After 200 iterations,the recognition rates on the verification set reach 96. 0% and 69. 9% respectively. Theexperimental results show that such model can effectively recognize the spatiotemporal features in video,and the recognition accuracy issignificantly improved compared with the previous recognition models. Such model can not only ensure that the features are not lost,butalso prevent the occurrence of over fitting,and the accuracy of recognition has been significantly improved,which proves that the model iseffective and feasible.

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更新日期/Last Update: 2023-04-10