20 August 2010 A quick scan and lane recognition algorithm based on positional distribution and edge features
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Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78202F (2010) https://doi.org/10.1117/12.866964
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China
Abstract
With the growing number of vehicles on the road, the automatic guided vehicles (AGV) vision system for intelligent vehicles has been given more and more attention. Lane recognition is an important component in the automatic guided vehicles (AGV) vision system for intelligent vehicles. To improve the speed and accuracy of lane recognition, this paper proposed an image segmentation algorithm based on the normalized histogram matching and a specific image scan algorithm based on positional distribution of lanes to reduce runtime. The purpose of image segmentation is extracting useful road information and the algorithm is segmenting the image by calculating the similarity of Cumulative Distribution Function (CDF) of normalization histogram. The main idea of image scan algorithm proposed in this paper is regarding the lanes that have been found as starting points and looking for the new lanes. Then we use a novel lane screen algorithm based on the left and right edges of lanes' geometric feature to remove invalid information and improve the accuracy and promote efficiency effectively. At last, a lane prediction algorithm is proposed to predict the farther lanes which may be lost due to treating as noises. After our tests, this algorithm has better robustness and higher efficiency.
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Jian Wang, Yuan Zhang, Xiaomin Chen, Xiaoying Shi, "A quick scan and lane recognition algorithm based on positional distribution and edge features", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78202F (20 August 2010); doi: 10.1117/12.866964; https://doi.org/10.1117/12.866964
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