Abstract
The obstacle edge detection technology used in the parking assistant system helps drivers avoid obstacles, especially for inexperienced drivers. Reversing image processing, as part of smart driving, must meet the parking requirements of real-time and accuracy. Processing system should convert the color images from CCD camera to grayscale, abate noise and immunity with non-linear median filtering. And the Sobel operator that of real-time and accuracy is used in edge detection; adaptive valve segmentation technique separates these points in time for improving Hough transform identification. In order to identify the obstacles for drivers in reversing at once, it also need to be given the edge lines thicker, warning color and superimposed displayed with the original image on the terminal screen, so that the whole process can make the parking assistant system more accurate, consistent and quickly to show information.
Highlights
The obstacle edge detection technology used in the parking assistant system helps drivers avoid obstacles, especially for inexperienced drivers
The detection operator in tαβ Color Space chromaticity based on Sobel operator proposed by the Ruderman is to balance of the integrated approach of color images on brightness, chromaticity-difference and saturation, which considers the allowance of the calculation of the vehicle [9]
For the new parking assistant system in this paper, we evaluate with the methods like reaction time test and subjective evaluation
Summary
The original images are acquired on the scene with different causes of noise which will flood the signal characteristics, while severing that is not contribute to the extraction of characteristic quantities. The main role of image pre-processing is to remove noise interference in order to get a clearer picture. The filtering methods commonly used are Linear and Non-linear method. Linear smoothing filter has two basic ways as the neighborhood average and weighted average. Linear filter can smooth the images and remove the noise, but some of the details in the image will become blurred. For images with continuous noise, linear filter is better, such as uniform or Gaussian additive noise, it (a) 3×3 median filtering (b) 5×5 median filtering
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