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Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network
Journal of Intelligent & Fuzzy Systems ( IF 2 ) Pub Date : 2020-09-09 , DOI: 10.3233/jifs-189290
Bing Zheng 1 , Dawei Yun 1 , Yan Liang 1
Affiliation  

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

中文翻译:

基于自动编码器特征融合与递归神经网络的行为识别研究

在COVID-19的影响下,迫切需要对行为识别进行研究。本文将自适应编码器算法与递归神经网络相结合,实现了行为模式识别的研究。目前,人类行为识别的研究大多集中在基于视频数量的视频数据上。同时,由于视频图像数据的复杂性,很容易侵犯个人隐私。随着物联网技术的飞速发展,它引起了众多专家学者的关注。研究人员已尝试使用许多机器学习方法,例如随机森林,支持向量机和其他浅层学习方法,它们在实验室环境中表现良好,但距离实际应用还有很长的路要走。
更新日期:2020-09-12
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