Guo et al 2015d - Revision history

Scipediacontent at 15:03, 21 January 2021

2021-01-21T15:03:13Z

Scipediacontent: Scipediacontent moved page Draft Content 681449950 to Guo et al 2015d

2020-09-29T07:58:25Z

Scipediacontent moved page Draft Content 681449950 to Guo et al 2015d

Scipediacontent: Created page with " == Abstract == Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane dete..."

2020-09-29T07:58:22Z

Created page with " == Abstract == Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane dete..."

New page

== Abstract ==

Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicle’s departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_681449950-beopen11352-8557-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [https://link.springer.com/content/pdf/10.1007%2F978-3-319-21969-1_20.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-319-21969-1_20.pdf]

* [http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20 http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20],[http://dx.doi.org/10.1007/978-3-319-21969-1_20 http://dx.doi.org/10.1007/978-3-319-21969-1_20] under the license http://www.springer.com/tdm

* [https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20 https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20],[http://dblp.uni-trier.de/db/conf/icig/icig2015-3.html#GuoZLLZ15 http://dblp.uni-trier.de/db/conf/icig/icig2015-3.html#GuoZLLZ15],[https://core.ac.uk/display/155696713 https://core.ac.uk/display/155696713],[https://academic.microsoft.com/#/detail/2287601897 https://academic.microsoft.com/#/detail/2287601897]

@@ Line 2: / Line 2: @@
 == Abstract ==
-Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicle’s departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm.
+Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicleâs departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm.
-−
-Document type: Part of book or chapter of book
-−
-== Full document ==
-<pdf>Media:Draft_Content_681449950-beopen11352-8557-document.pdf</pdf>
@@ Line 16: / Line 11: @@
 * [https://link.springer.com/content/pdf/10.1007%2F978-3-319-21969-1_20.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-319-21969-1_20.pdf]
-* [http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20 http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20],[http://dx.doi.org/10.1007/978-3-319-21969-1_20 http://dx.doi.org/10.1007/978-3-319-21969-1_20] under the license http://www.springer.com/tdm
+* [http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20 http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20],
-* [https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20 https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20],[http://dblp.uni-trier.de/db/conf/icig/icig2015-3.html#GuoZLLZ15 http://dblp.uni-trier.de/db/conf/icig/icig2015-3.html#GuoZLLZ15],[https://core.ac.uk/display/155696713 https://core.ac.uk/display/155696713],[https://academic.microsoft.com/#/detail/2287601897 https://academic.microsoft.com/#/detail/2287601897]
+* [https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20 https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20],

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