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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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* [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] | ||
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| + | * [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] | ||
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| + | * [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] | ||
Latest revision as of 16:31, 21 January 2021
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.
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Published on 01/01/2014
Volume 2014, 2014
DOI: 10.1007/978-3-642-53842-1_6
Licence: CC BY-NC-SA license
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