Road Detection Based on MS-RG Hybrid Image Segmentation Model

被引：0

作者：

Liu B.-S. ^{[1
]}

Lv Y.-B. ^{[1
]}

Lv W.-J. ^{[1
]}

Li J. ^{[1
]}

Ouyang Q. ^{[1
]}

机构：

[1] School of Traffic and Transportation, Beijing Jiaotong University, Beijing

来源：

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2019年 / 19卷 / 02期

基金：

国家重点研发计划; 中国国家自然科学基金;

关键词：

Image segmentation; Intelligent transportation; Mean shift; Region growing; Road detection;

D O I：

10.16097/j.cnki.1009-6744.2019.02.009

中图分类号：

学科分类号：

摘要：

Due to the diversity of its structure, the complexity of texture changes and the instability of natural exposure, road scenes based on road segmentation mostly have information redundancy, and there are quality problems such as boundary loss and blur. In this paper, we first used the Meanshift algorithm on the road image to find the local optimum by the trapezoidal rise of the probability density in the space, and search for the pixels with the same modulus and then get together to form the super pixel block. Then, the clustering super-pixel block obtained by the Meanshift algorithm was used to perform a variety of sub-point region growth, standardized the growth rule, overcome the defect that the closed boundary cannot be obtained, improve the segmentation effect of the road image. The experimental results show that the proposed method has strong applicability, and it can effectively improve the segmentation accuracy and real-time performance compared with the traditional method, and can accurately identify the road information in the image to ensure that the vehicle can travel on the travelable area. Copyright © 2019 by Science Press.

引用

页码：60 / 65

页数：5

共 16 条

[1]

Lu D.B., Guo Z.M., Cai B.G., Et al., A vehicle detection and tracking method based on range data, Journal of Transportation Systems Engineering and Information Technology, 18, 3, pp. 55-62, (2018)

[2]

Kostas H., Serafim N.E., Hybrid image segmentation using watersheds and fast region merging, IEEE Transactions on Image Processing, 7, 12, pp. 1684-1699, (1998)

[3]

Road segmentation model based on fusion via hierarchical conditional random field, Robot, pp. 1-14

[4]

Comaniciu D., Meer P., Mean shift: A robust approach toward feature space analysis, IEEE Trans Pattern Analysis & Machine Intelligence, 24, 5, pp. 603-619, (2002)

[5]

Sumengen B., Tesic J., Normalized cuts and image segmentation, ECE, 278, pp. 1-7, (2000)

[6]

Moore A.P., Et al., Superpixel lattices, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1-8, (2008)

[7]

Liu M.Y., Tuzel O., Ramalingam S., Et al., Entropy rate superpixel segmentation, 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097-2104, (2011)

[8]

Achanta, Radhakrishna, Et al., SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1, pp. 2274-2282, (2012)

[9]

Lucchi A., Smith K., Supervoxel-based segmentation of mitochondria in EM image stacks with learned shape features, IEEE Transactions on Medical Imaging, 31, 2, pp. 474-486, (2012)

[10]

Wang S., Lu H., Ang F., Et al., Superpixel tracking, IEEE International Conference on Computer Vision (ICCV), pp. 1323-1330, (2011)

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