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Author:

Wu Tao (Wu Tao.) | Zhang Quan (Zhang Quan.) | Wang Zhanhai (Wang Zhanhai.)

Indexed by:

CPCI-S EI Scopus

Abstract:

In order to solve the problem that the visual observation actions of the tower controller during the take-off and ground-off phases of the aircraft can only be recognized manually by the management personnel, the visual observation action recognition software of the tower controller is established. In the PyCharm compilation environment, based on Python, call functions in the OpenCV library to obtain video frames, use functions in the Dlib library to implement face detection, implement face recognition based on the Caffe framework, and implement face rotation tracking recognition based on CamShift functions. Display the results of the visual observation action recognition of the tower controller in graphic form. The results show that the software has the advantage of extracting the deep features of the image, realizes the function of automatically identifying the visual observation actions of the tower controller, with high recognition accuracy, and provides a powerful automated tool for tower site management and incident investigation.

Keyword:

Visual Observation Tower Controller Face Recognition Python Open CV

Author Community:

  • [ 1 ] [Wu Tao]China Acad Civil Aviat Sci & Technol, Aviat Accid Invest Ctr CAAC, Beijing, Peoples R China
  • [ 2 ] [Wang Zhanhai]China Acad Civil Aviat Sci & Technol, Aviat Accid Invest Ctr CAAC, Beijing, Peoples R China
  • [ 3 ] [Zhang Quan]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China

Reprint Author's Address:

  • [Wu Tao]China Acad Civil Aviat Sci & Technol, Aviat Accid Invest Ctr CAAC, Beijing, Peoples R China

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Source :

PROCEEDINGS OF 2020 IEEE 2ND INTERNATIONAL CONFERENCE ON CIVIL AVIATION SAFETY AND INFORMATION TECHNOLOGY (ICCASIT)

Year: 2020

Page: 992-995

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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