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Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network.
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- Author(s): Zheng, Bing1 (AUTHOR); Yun, Dawei1 (AUTHOR) ; Liang, Yan1 (AUTHOR); Li, Xiaolong (AUTHOR)
- Source:
Journal of Intelligent & Fuzzy Systems. 2020, Vol. 39 Issue 6, p8927-8935. 9p.- Subject Terms:
- Source:
- Additional Information
- Abstract: 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. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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