Page 181 - Kaleidoscope Academic Conference Proceedings 2020

Industry-driven digital transformation

AI-based suspicious object recognition technologies for this filtering for internet of medical things,” IEEE Access,
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suspicious objects. To achieve this goal, we built a suspicious
[9] C. Ledig, L. Theis, F. Huszár, J. Caballero,
object database for CNN training by GAN and active/passive
A. Cunningham, A. Acosta, A. Aitken, A. Tejani,
imagers. In the evaluation section, we prove how to get
J. Totz, Z. Wang, and W. Shi, “Photo-realistic single
an optimal recognition rate when the original images used
image super-resolution using a generative adversarial
for CNN training are not enough. This work is significant for
network,” in 2017 IEEE Conference on Computer Vision
improving the service quality of AI-based W-band suspicious
and Pattern Recognition (CVPR), 2017, pp. 105–114.
object detection systems for moving persons. Moreover, we
introduce the corresponding standardization activities.
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ACKNOWLEGEMENT
single-chip w-band fmcw radar modules for
commercial high-resolution sensor applications,”
This work was supported by the Research Grant for
IEEE Transactions on Microwave Theory and
Expanding Radio Wave Resources in FY2020 of the Ministry
Techniques, vol. 50, no. 12, pp. 2995–3001, 2002.
of Internal Affairs and Communications through a Contract
for Research and Development of Radar Fundamental [11] C. Wang, C. Xu, X. Yao, and D. Tao, “Evolutionary
Technology for Advanced Recognition of Moving Objects generative adversarial networks,” IEEE Transactions on
for Security Enhancement under Grant JPJ000254. Evolutionary Computation, vol. 23, no. 6, pp. 921–934,
2019.
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