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Research on Attention Analysis Based on Vision

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Artificial Intelligence in China

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 854))

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Abstract

Attention analysis technology refers to the analysis of people's attention during work or study through monitoring various data. This paper proposed a visual-based attention analysis method, in which a dataset for attention analysis was built and the neural network was adopted to analyze attention. Good results had been achieved on the self-built dataset with a 93% recognition rate.

This work is supported by Research and Development of Attention Device Based on Brain Science and Machine Vision, Dalian Youth Science and Technology Star 2019(2019RQ125); Key R & D projects of Liaoning Province Education Department (Research on Sensors for Pelvic Floor Rehabilitation of Puerpera Based on Artificial Intelligence).

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Dalian Neusoft University of Information, Liaoning, 116023, China

    Ge Changyun, Han Ti, Zhang Yuanyuan, Yao Yifei, Li Ning, Zhou Yuhao, Zhang Xiaomeng & Shen Jian

Authors
  1. Ge Changyun
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  2. Han Ti
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  3. Zhang Yuanyuan
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  4. Yao Yifei
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  5. Li Ning
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  6. Zhou Yuhao
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  7. Zhang Xiaomeng
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  8. Shen Jian
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Wei Wang

  3. Tianjin Normal University, Tianjin, China

    Jiasong Mu

  4. Dalian University of Technology, Dalian, China

    Xin Liu

  5. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Changyun, G. et al. (2022). Research on Attention Analysis Based on Vision. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_32

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  • DOI: https://doi.org/10.1007/978-981-16-9423-3_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9422-6

  • Online ISBN: 978-981-16-9423-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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