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Revisit Surround-view Camera System Calibration

Shao, Xuan (author)
Tongji University
Liu, Xiao (author)
Tongji University
Zhang, Lin (author)
Tongji University
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Zhao, Shengjie (author)
Tongji University
Shen, Ying (author)
Tongji University
Yang, Yukai, PhD. Universitetslektor, 1979- (author)
Uppsala universitet,Statistiska institutionen
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 (creator_code:org_t)
Shanghai : Institute of Electrical and Electronics Engineers (IEEE), 2019
2019
English.
In: 2019 IEEE International Conference on Multimedia and Expo (ICME). - Shanghai : Institute of Electrical and Electronics Engineers (IEEE). - 9781538695524 - 9781538695531 ; , s. 1486-1491
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • The surround-view system is an essential component of an advanced driver assistance system especially when the vehicle runs in tight parking space or on a narrow road. To ensure successful maneuvering, a panoramic bird's-eye image with no blind spots is necessarily called for. Hence, a typical surround-view system consists of several cameras mounted around the vehicle capturing images from a top-down viewpoint, and an accurate extrinsic calibration for such system is prerequisite for providing a seamless surround-view image. To achieve this goal, this paper presents a novel extrinsic calibration pipeline which is both easy-to-use and reliable to operate on multiple cameras. Instead of taking the vehicle to a fixed position in a specific calibration site, a single chessboard is the only demand. We adopt a novel refinement procedure that jointly optimizes camera poses in a closed-loop manner. The effectiveness and efficiency of the proposed pipeline to calibrate a surround-view camera system has been corroborated by experiments.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Keyword

Cameras
Calibration
Pipelines
Optimization
Vehicles
Advanced driver assistance systems
Feature extraction

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