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Descripción
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| Intelligent Transportation Systems (ITS) allow us to have high quality traffic information to reduce the risk of potentially critical situations. Conventional image-based traffic detection methods have difficulties acquiring good images due to perspective and background noise, poor lighting and weather conditions. In this paper, we propose a new method to accurately segment and track vehicles. After removing perspective using Modified Inverse Perspective Mapping (MIPM), Hough transform is applied to extract road lines and lanes. Then, Gauss- ian Mixture Models (GMM) are used to segment moving objects and to tackle car shadow effects, we apply a chromacity-based strategy. Finally, performance is evaluated through three different video benchmarks: own recorded videos in Madrid and Tehran (with different weather conditions at urban and interurban areas); and two well-known public datasets (KITTI and DETRAC). Our results indicate that the proposed algorithms are robust, and more accurate compared to others, especially when facing occlusions, lighting variations and | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Plos One |
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ISSN
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1932-6203 |
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Factor de impacto JCR
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2,806 |
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Información de impacto
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Datos JCR del año 2016 |
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Volumen
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13 |
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DOI
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10.1371/journal.pone.0191355 |
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Número de revista
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3 |
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Desde la página
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1 |
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Hasta la página
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30 |
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Mes
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MARZO |
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Ranking
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