We developed an obstacle detection system based on our proposed fisheye lens inverse perspective mapping method. In this thesis of fisheye lens inverse perspective mapping method, we first point out the error which former researcher made and then we proposed a new normal lens inverse perspective mapping equations. With our normal lens inverse perspective mapping equations, a vertical straight line in the original image will be projected to a straight line whose prolongation will pass the camera vertical projection point on the remapped image and a horizontal straight line in the original image will be projected to a straight line on the remapped image. To take fisheye lens distortion effect into account, we combine a modified spherical fisheye lens model with the modified normal lens inverse perspective mapping to be suitable for fisheye lens case.
The method in this thesis of obstacle detection algorithm, we utilize the feature of object’s vertical edges to indicate the position of obstacles. Since we have obtained the remapped image, we will judge the static situation of current frame and choose one among the profile image and temporal inverse perspective mapping difference image.
To obtain the profile image, we execute sharpening, edge detection, morphological operation, and modified thinning algorithm. In addition, we perform a spatial shift on former frame remapped image to obtain the temporal inverse perspective mapping difference image. After feature searching stage, a polar histogram which represents the position and length of feature segment will be drawn simultaneously. The histogram post-processing procedure will exclude the planar lane marking segments and noise.
By tracking and confirming the obstacle candidates, the position of obstacle with respect to our vehicle will be known. If the obstacle is approaching our vehicle, the
system will warn the driver to be attentive.
In the future, our obstacle detection system can be integrated into around vehicle collision warning system and lane departure warning system to increase the driving safety on the road. Furthermore, we can research the different shape of obstacles to detect not only quasi-vertical edge obstacles but all sorts of defined obstacles. Speeds up the system operation speed for real time use is also an important issue in the future.
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