• Title/Summary/Keyword: 헬멧자세 추적시스템

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Design and Evaluation of Intelligent Helmet Display System (지능형 헬멧시현시스템 설계 및 시험평가)

  • Hwang, Sang-Hyun
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.45 no.5
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    • pp.417-428
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    • 2017
  • In this paper, we describe the architectural design, unit component hardware design and core software design(Helmet Pose Tracking Software and Terrain Elevation Data Correction Software) of IHDS(Intelligent Helmet Display System), and describe the results of unit test and integration test. According to the trend of the latest helmet display system, the specifications which includes 3D map display, FLIR(Forward Looking Infra-Red) display, hybrid helmet pose tracking, visor reflection type of binocular optical system, NVC(Night Vision Camera) display, lightweight composite helmet shell were applied to the design. Especially, we proposed unique design concepts such as the automatic correction of altitude error of 3D map data, high precision image registration, multi-color lighting optical system, transmissive image emitting surface using diffraction optical element, tracking camera minimizing latency time of helmet pose estimation and air pockets for helmet fixation on head. After completing the prototype of all system components, unit tests and system integration tests were performed to verify the functions and performance.

Design and Implementation of Real-Time Helmet Pose Tracking System (실시간 헬멧자세 추적시스템의 설계 및 구현)

  • Hwang, Sang-Hyun;Chung, Chul-Ju;Kim, Dong-Sung
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.44 no.2
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    • pp.123-130
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    • 2016
  • This paper describes the design and implementation scheme of HTS(Helmet Tracking System) providing coincident LOS(Line of Sight) between aircraft and HMD(Helmet Mounted Display) which displays flight and mission information on Pilot helmet. The functionality and performance of HMD system depends on the performance of helmet tracking system. The target of HTS system design is to meet real-time performance and reliability by predicting non-periodic latency and high accuracy performance. To prove an availability of a proposed approach, a robust hybrid scheme with a fusion optical and inertial tracking system are tested through a implemented test-bed. Experimental results show real-time and reliable tracking control in spite of external errors.

Implementation of a Helmet Azimuth Tracking System in the Vehicle (이동체 내의 헬멧 방위각 추적 시스템 구현)

  • Lee, Ji-Hoon;Chung, Hae
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.4
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    • pp.529-535
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    • 2020
  • It is important to secure the driver's external field view in armored vehicles surrounded by iron armor for preparation for the enemy's firepower. For this purpose, a 360 degree rotatable surveillance camera is mounted on the vehicle. In this case, the key idea is to recognize the head of the driver wearing a helmet so that the external camera rotated in exactly the same direction. In this paper, we introduce a method that uses a MEMS-based AHRS sensor and a illuminance sensor to compensate for the disadvantages of the existing optical method and implements it with low cost. The key idea is to set the direction of the camera by using the difference between the Euler angles detected by two sensors mounted on the camera and the helmet, and to adjust the direction with illuminance sensor from time to time to remove the drift error of sensors. The implemented prototype will show the camera's direction matches exactly in driver's one.

Implementation of an alarm system with AI image processing to detect whether a helmet is worn or not and a fall accident (헬멧 착용 여부 및 쓰러짐 사고 감지를 위한 AI 영상처리와 알람 시스템의 구현)

  • Yong-Hwa Jo;Hyuek-Jae Lee
    • Journal of the Institute of Convergence Signal Processing
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    • v.23 no.3
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    • pp.150-159
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    • 2022
  • This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.