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Recent Trends in Communication, Computing, and Electronics pp 345–356Cite as

Enabling More Accurate Bounding Boxes for Deep Learning-Based Real-Time Human Detection

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 524)

Abstract

While human detection has been significantly recognized and widely used in many areas, the importance of human detection for behavioral analysis in medical research has been rarely reported. Recently, however, efforts have been actively made to recognize behavior diseases by measuring gait variability using pattern analysis of human detection results from videos taken by cameras. For this purpose, it is very crucial to establish robust human detection algorithms. In this work, we modified deep learning models by changing multi-detection into human detection. Also, we improved the localization of human detection by adjusting the input image according to the ratio of objects in an image and improving the results of several bounding boxes by interpolation. Experimental results demonstrated that by adopting the proposals, the accuracy of human detection could be increased significantly.

Keywords

  • Human detection
  • Deep learning
  • Bounding box regression
  • Localization
  • Real-time analysis

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Acknowledgements

This work was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2016M3C7A1905477, NRF-2014M3C7A1046050) and the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2017R1D1A1B03036423). This study was approved by the Institutional Review Board of Gwangju Institute of Science and Technology (IRB no. 20180629-HR-36-07-04). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123, Cheomdangwagi-ro, Buk-gu, 61005, Gwangju, Korea

    Hyunsu Jeong, Cheolbin Park & Jong-In Song

  2. Biomedical Research Institute, Seoul National University Hospital (SNUH), Seoul, 03080, Korea

    Jeonghwan Gwak

  3. Department of Radiology, Seoul National University Hospital (SNUH), Seoul, 03080, Korea

    Jeonghwan Gwak

  4. Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat, India

    Manish Khare

  5. Centre of Computer Education, Institute of Professional Studies, University of Allahabad, Allahabad, India

    Om Prakash

Authors
  1. Hyunsu Jeong
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  2. Jeonghwan Gwak
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  3. Cheolbin Park
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  4. Manish Khare
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  5. Om Prakash
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  6. Jong-In Song
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Corresponding authors

Correspondence to Jeonghwan Gwak or Jong-In Song .

Editor information

Editors and Affiliations

  1. University of Allahabad, Allahabad, India

    Dr. Ashish Khare

  2. Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India

    Prof. Uma Shankar Tiwary

  3. Oakland University, Rochester, MI, USA

    Prof. Ishwar K. Sethi

  4. University of Allahabad, Allahabad, Uttar Pradesh, India

    Prof. Nar Singh

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Jeong, H., Gwak, J., Park, C., Khare, M., Prakash, O., Song, JI. (2019). Enabling More Accurate Bounding Boxes for Deep Learning-Based Real-Time Human Detection. In: Khare, A., Tiwary, U., Sethi, I., Singh, N. (eds) Recent Trends in Communication, Computing, and Electronics. Lecture Notes in Electrical Engineering, vol 524. Springer, Singapore. https://doi.org/10.1007/978-981-13-2685-1_33

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  • DOI: https://doi.org/10.1007/978-981-13-2685-1_33

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