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Feature Extraction for Eye Movement Video Data

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International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019 (ATCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1017))

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Abstract

Random objects in videos are common stimuli in eye tracker based studies and their locations and time of appearance need to be detected in related research such as depression detection. In this paper, we propose a new method to extract features in eye movement video data captured by the SMI eye tracker. Firstly, we provide a feature extraction method by using the circle Hough transform and the Douglas–Peucker algorithm to extract the feature for each frame of the eye movement video data, and verify its validity in eye movement video data processing. Secondly, because the storage time of the eye tracker is more accurate than the on-screen time of the exported video, we choose to extract the storage time of the eye tracker to improve the quality of feature extraction. Finally, we add batch processing function to improve the efficiency of the experiment. Experimental results show that the method can extract the eye movement features in the eye movement video accurately and effectively.

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Acknowledgments

This research was supported by Shandong Provincial Natural Science Foundation, China (Grant No: ZR2016FM14), the National Natural Science Foundation of China (Grant No: 81573829, 61703219).

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Authors and Affiliations

  1. School of Computer Science and Technology, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China

    Yifang Yuan & Qingxiang Wang

Authors
  1. Yifang Yuan
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  2. Qingxiang Wang
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Corresponding author

Correspondence to Qingxiang Wang .

Editor information

Editors and Affiliations

  1. Faculty of Science, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  4. Shanghai University, Shanghai, China

    Zheng Xu

  5. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

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Yuan, Y., Wang, Q. (2020). Feature Extraction for Eye Movement Video Data. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_15

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