A Fully Automatic Approach for Fisheye Camera Calibration

Tai, Yen-Chou; Hsieh, Yi-Yu; Chuang, Jen-Hui

doi:10.1109/vcip.2018.8698621

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of multiple camera systems used on ADAS platforms, the calibration of an individual sensor unit demands a well-ordered approach and an extra amount of tuning time. With the increase in the demand for exploitation of larger FOV cameras on ADAS platforms [8], industries and companies tend to explore more feasible approaches to calibrate such lens, eluding human intervention and extensive manual labor [9]. Specifically, automobiles are often exposed to various temperatures and climatic conditions which can heavily influence the dynamics of intrinsic parameters of the cameras attached to them [10,11].…”

Section: Introductionmentioning

confidence: 99%

Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors for the Advanced Driver-Assistance System (ADAS)

Kakani

Kim

Kumbham

et al. 2019

Sensors

View full text Add to dashboard Cite

This paper proposes a self-calibration method that can be applied for multiple larger field-of-view (FOV) camera models on an advanced driver-assistance system (ADAS). Firstly, the proposed method performs a series of pre-processing steps such as edge detection, length thresholding, and edge grouping for the segregation of robust line candidates from the pool of initial distortion line segments. A novel straightness cost constraint with a cross-entropy loss was imposed on the selected line candidates, thereby exploiting that novel loss to optimize the lens-distortion parameters using the Levenberg–Marquardt (LM) optimization approach. The best-fit distortion parameters are used for the undistortion of an image frame, thereby employing various high-end vision-based tasks on the distortion-rectified frame. In this study, an investigation was carried out on experimental approaches such as parameter sharing between multiple camera systems and model-specific empirical γ -residual rectification factor. The quantitative comparisons were carried out between the proposed method and traditional OpenCV method as well as contemporary state-of-the-art self-calibration techniques on KITTI dataset with synthetically generated distortion ranges. Famous image consistency metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and position error in salient points estimation were employed for the performance evaluations. Finally, for a better performance validation of the proposed system on a real-time ADAS platform, a pragmatic approach of qualitative analysis has been conducted through streamlining high-end vision-based tasks such as object detection, localization, and mapping, and auto-parking on undistorted frames.

show abstract

Section: Introductionmentioning

confidence: 99%