Abstract
The study proposes an outlier refinement methodology for automatic distortion rectification of wide-angle and fish-eye lens camera models in the context of streamlining vision-based tasks. The line-members sets are estimated in a scene through accumulation of line candidates emerging from the same edge source. An iterative optimization with an outlier refinement scheme was applied to the loss value, to simultaneously remove the extremely curved outliers from the line-members set and update the robust line members as well as estimating the best-fit distortion parameters with lowest possible loss. The proposed algorithm was able to rectify the distortions of wide-angle and fish-eye cameras even in extreme conditions such as heavy illumination changes and severe lens distortions. Experiments were conducted using various evaluation metrics both at the pixel-level (image quality, edge stretching effects, pixel-point error) as well as higher-level use-cases (object detection, height estimation) with respect to real and synthetic data from publicly available, privately acquired sources. The performance evaluations of the proposed algorithm have been investigated using an ablation study on various datasets in correspondence to the significance analysis of the refinement scheme and loss function. Several quantitative and qualitative comparisons were carried out on the proposed approach against various self-calibration approaches.
Highlights
The usage of wide-angle camera lenses in vision-based applications demands greater precision in terms of image projection geometry such as distortion compensation and maintaining pixel consistency.There appears to be a plethora of challenges involved in the context of employing wide-angle lens models for applications such as advanced driver-assistance system (ADAS) and video surveillance.The image projections from the wide-angle and fish-eye lens are generally affected by the radial distortions and thereby create a scenario of severe pixel inconsistencies along the edges which depend on the properties of the lens such as horizontal FOV, curvature, etc. [1,2]
This influences the performance of the lens employed in various metric-based tasks such as height estimation and single metrology, and even in geometrical tasks such as camera localization, stereo-vision, etc
This study focuses mostly on the drawbacks encountered in our previous work [19] and proposes a solution to handle heavy distortions without having to use any model-specific residual factors
Summary
The usage of wide-angle camera lenses in vision-based applications demands greater precision in terms of image projection geometry such as distortion compensation and maintaining pixel consistency.There appears to be a plethora of challenges involved in the context of employing wide-angle lens models for applications such as advanced driver-assistance system (ADAS) and video surveillance.The image projections from the wide-angle and fish-eye lens are generally affected by the radial distortions and thereby create a scenario of severe pixel inconsistencies along the edges which depend on the properties of the lens such as horizontal FOV, curvature, etc. [1,2]. The image projections from the wide-angle and fish-eye lens are generally affected by the radial distortions and thereby create a scenario of severe pixel inconsistencies along the edges which depend on the properties of the lens such as horizontal FOV, curvature, etc. [1,2] This influences the performance of the lens employed in various metric-based tasks such as height estimation and single metrology, and even in geometrical tasks such as camera localization, stereo-vision, etc. The presence of various larger FOV lens models such as fish-eye (165◦ < FOV < 190◦ ), wide-angle (120◦ < FOV < 150◦ ), and super wide-angle (160◦ < FOV < 180◦ )
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