A Hough-Space-Based Automatic Online Calibration Method for a Side-Rear-View Monitoring System

Abstract

We propose an automatic camera calibration method for a side-rear-view monitoring system in natural driving environments. The proposed method assumes that the camera is always located near the surface of the vehicle so that it always shoots a part of the vehicle. This method utilizes photographed vehicle information because the captured vehicle always appears stationary in the image, regardless of the surrounding environment. The proposed algorithm detects the vehicle from the image and computes the similarity score between the detected vehicle and the previously stored vehicle model. Conventional online calibration methods use additional equipment or operate only in specific driving environments. On the contrary, the proposed method is advantageous because it can automatically calibrate camera-based monitoring systems in any driving environment without using additional equipment. The calibration range of the automatic calibration method was verified through simulations and evaluated both quantitatively and qualitatively through actual driving experiments.

A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

“…Lee et al [28] proposed a camera calibration method detecting the host vehicle instead of detecting the road lanes. More specifically, this method detects the host vehicle surface to avoid the problems of utilizing detected road lanes, but it can only estimate the orientation of the camera.…”

“…Other approaches detect road lanes or the host vehicle instead of utilizing addi devices [24][25][26][27][28]. These approaches also focus on the calibration of only one camera et al [29] calibrated four AVM cameras to align adjacent images using detected lanes.…”