Rezaei Terauchi 2014a - Revision history

Scipediacontent at 15:31, 21 January 2021

2021-01-21T15:31:10Z

Scipediacontent: Scipediacontent moved page Draft Content 375907875 to Rezaei Terauchi 2014a

2020-10-14T12:35:09Z

Scipediacontent moved page Draft Content 375907875 to Rezaei Terauchi 2014a

Scipediacontent: Created page with " == Abstract == On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision..."

2020-10-14T12:35:06Z

Created page with " == Abstract == On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision..."

New page

== Abstract ==

On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision-based vehicle detection under challenging lighting conditions, being still an open topic in the area of driver assistance systems. The paper proposes an effective vehicle detection method based on multiple features analysis and Dempster-Shafer-based fusion theory. We also utilize a new idea of Adaptive Global Haar-like (AGHaar) features as a promising method for feature classification and vehicle detection in both daylight and night conditions. Validation tests and experimental results show superior detection results for day, night, rainy, and challenging conditions compared to state-of-the-art solutions.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_375907875-beopen578-2489-document.pdf</pdf>

== Original document ==

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

* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-53842-1_6.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-53842-1_6.pdf]

← Older revision		Revision as of 15:31, 21 January 2021
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	On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision-based vehicle detection under challenging lighting conditions, being still an open topic in the area of driver assistance systems. The paper proposes an effective vehicle detection method based on multiple features analysis and Dempster-Shafer-based fusion theory. We also utilize a new idea of Adaptive Global Haar-like (AGHaar) features as a promising method for feature classification and vehicle detection in both daylight and night conditions. Validation tests and experimental results show superior detection results for day, night, rainy, and challenging conditions compared to state-of-the-art solutions.		On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision-based vehicle detection under challenging lighting conditions, being still an open topic in the area of driver assistance systems. The paper proposes an effective vehicle detection method based on multiple features analysis and Dempster-Shafer-based fusion theory. We also utilize a new idea of Adaptive Global Haar-like (AGHaar) features as a promising method for feature classification and vehicle detection in both daylight and night conditions. Validation tests and experimental results show superior detection results for day, night, rainy, and challenging conditions compared to state-of-the-art solutions.
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−	~~Document type: Part of book or chapter of book~~
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−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_375907875-beopen578-2489-document.pdf</pdf>~~


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	* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-53842-1_6.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-53842-1_6.pdf]		* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-53842-1_6.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-53842-1_6.pdf]
		+
		+	* [http://link.springer.com/content/pdf/10.1007/978-3-642-53842-1_6 http://link.springer.com/content/pdf/10.1007/978-3-642-53842-1_6],
		+	: [http://dx.doi.org/10.1007/978-3-642-53842-1_6 http://dx.doi.org/10.1007/978-3-642-53842-1_6]
		+
		+	* [https://link.springer.com/chapter/10.1007/978-3-642-53842-1_6 https://link.springer.com/chapter/10.1007/978-3-642-53842-1_6],
		+	: [https://www.scipedia.com/public/Rezaei_Terauchi_2014a https://www.scipedia.com/public/Rezaei_Terauchi_2014a],
		+	: [https://dblp.uni-trier.de/db/conf/psivt/psivt2013.html#RezaeiT13 https://dblp.uni-trier.de/db/conf/psivt/psivt2013.html#RezaeiT13],
		+	: [https://rd.springer.com/chapter/10.1007/978-3-642-53842-1_6 https://rd.springer.com/chapter/10.1007/978-3-642-53842-1_6],
		+	: [https://academic.microsoft.com/#/detail/1472232700 https://academic.microsoft.com/#/detail/1472232700]

← Older revision	Revision as of 12:35, 14 October 2020
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