A lane line detecting and tracking and detecting method

Abstract

The invention discloses a lane line detecting and tracking and detecting method, and relates to the technical field of image processing. The lane line detecting method of the invention comprises: performing an inverse perspective transform processing on a preprocessed image based a homography matrix H, performing a blocking and straight line detection on the processed image, and then making endpoints of straight lines of the upmost image block and the minimum image block as match points to obtain a smooth curve x, and based on weight of the curve x, selecting a curve with the maximum weight as a lane line pair of a current frame image. The lane line tracking and detecting method of the invention comprises: firstly outputting a lane line pair of a first frame image based on the above detecting method, wherein a lane line of a subsequent frame is determined by a target region of a current frame detected by a loop-structured tracker, and a parameter of the tracker is derived from a lane line coordinate of the former frame; outputting a frame by the above detecting method in case of a detection error. The method is applicable for lane line detecting, and the method has strong robustness, great instantaneity and high recognition rate.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to the detection of lane lines. Background technique [0002] Every year, many people around the world are killed in car accidents due to vehicle deviation. In order to avoid such things from happening, people have proposed an intelligent assisted driving system, which can warn the driver to a certain extent and reduce such accidents. the effect that occurs. Lane line detection is an important part of the intelligent driving assistance system. A series of algorithms with good results have been proposed in this field, including template-based, texture-based, and region-based algorithms. [0003] Due to real-time constraints, even some well-known algorithms have high detection and tracking rates, but because they require a lot of processing time, it is difficult to apply them in practice, such as Yue Wang using B-Snake for lane line detection algorithm. Some al...

AI Technical Summary

Problems solved by technology

[0003] Due to real-time constraints, even some well-known algorithms have high detection and tracking rates, but because they require a lot of processing time, it is difficult to apply them in practice, such as Yue Wang using B-Snake for lane line detection algorithm

Some algorithms can achieve better detection results in the case of weak light and missing lane lines, but have a high error rate for curve detection, such as Massimo Bertozzi's algorithm for lane line detection using a stereo vision system

[0004] Some other algorithms, such as Canny / Hough Estimation of Vanishing Points (CHEVP), locate the lane line by determining the position of the vanishing point on the lane detection picture, but the robustness of determining the lane line through the vanishing point in this algorithm is very low. Weak, because the algorithm assumes that the car lines in each area intersect at the same vanishing point, but due to noise and other interference, the real lane lines in each area do not intersect at the same vanishing point, so As for its weak robustness, in addition, the algorithm cannot deal with the absence of lane lines, and is sensitive to initialization parameters, and its robustness is weak

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