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

Abstract

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.

Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks

“…The significance of the novel aspects-such as loss aggregation over line-member sets-of the outlier refinement scheme was extensively tested through ablation study, and the corresponding results are discussed in Section 4. The major difference between our previous work [19] and the current study is as follows:…”

“…However, the heavy dependency on hyper-parameters and disability to handle samples acquired using low-quality camera sensors under low-light conditions make it less reliable for ADAS and video surveillance applications. Although, the algorithm proposed by Kakani et al [19,20] was able to rectify multiple lens models which include a wide-angle and fish-eye lens. Yet, the schematic includes model-specific empirical γ-residual rectification factor for heavy fish-eye distortions with FOV > 165 • .…”

“…The details of the summarized state-of-the-art automatic distortion rectification techniques are stated in Table 1. 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. Especially, this work introduces the outlier refinement scheme in conjunction with the plumbline angular loss function that makes the whole system more robust to outliers and thereby able to handle heavy distortions FOV > 190 • .…”

“…The segregation of robust line candidates was done on the basis of threshold heuristics in the previous work [19], which made some outliers raise some complications while dealing with heavy distortions FOV > 165 • , thereby creating a need for model-specific residual factors.…”

“…Unlike [19], the current study employs an iterative outlier refinement scheme which basically considers the aggregation of robust line members into a set and iterating the sets over the plumbline angular loss constraint. The loss over the cumulative line-member sets and corresponding estimated distortion parameters are used to eliminate the outliers, thereby using the new set of robust line candidates to update parameters for distortion rectification.…”