Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights
Nighttime traffic surveillance is difficult due to insufficient and unstable appearance information and strong background interference. We present in this paper a robust nighttime vehicle detection system by detecting, tracking, and grouping headlights. First, we train AdaBoost classifiers for headl...
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| Published in | IEEE transactions on intelligent transportation systems Vol. 16; no. 5; pp. 2838 - 2849 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.10.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | Nighttime traffic surveillance is difficult due to insufficient and unstable appearance information and strong background interference. We present in this paper a robust nighttime vehicle detection system by detecting, tracking, and grouping headlights. First, we train AdaBoost classifiers for headlights detection to reduce false alarms caused by reflections. Second, to take full advantage of the complementary nature of grouping and tracking, we alternately optimize grouping and tracking. For grouping, motion features produced by tracking are used by headlights pairing. We use a maximal independent set framework for effective pairing, which is more robust than traditional pairing-by-rules methods. For tracking, context information provided by pairing is employed by multiple object tracking. The experiments on challenging datasets and quantitative evaluation show promising performance of our method. |
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| AbstractList | Nighttime traffic surveillance is difficult due to insufficient and unstable appearance information and strong background interference. We present in this paper a robust nighttime vehicle detection system by detecting, tracking, and grouping headlights. First, we train AdaBoost classifiers for headlights detection to reduce false alarms caused by reflections. Second, to take full advantage of the complementary nature of grouping and tracking, we alternately optimize grouping and tracking. For grouping, motion features produced by tracking are used by headlights pairing. We use a maximal independent set framework for effective pairing, which is more robust than traditional pairing-by-rules methods. For tracking, context information provided by pairing is employed by multiple object tracking. The experiments on challenging datasets and quantitative evaluation show promising performance of our method. |
| Author | Qi Zou Haibin Ling Siwei Luo Mei Tian Yaping Huang |
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| Cites_doi | 10.1109/6979.880969 10.1287/opre.42.5.860 10.1109/CCECE.2003.1226124 10.1002/nav.3800020109 10.1109/TITS.2011.2175219 10.1109/TITS.2010.2045375 10.1109/TITS.2011.2165338 10.1109/ICASSP.2002.5745462 10.1016/j.patcog.2007.05.017 10.1109/ICASSP.2013.6638042 10.1109/TITS.2011.2113340 10.1109/IVS.2008.4621142 10.1109/TITS.2013.2283302 10.1109/TPAMI.2008.146 10.1155/2008/246309 10.1109/IVS.2005.1505115 10.1023/B:VISI.0000013087.49260.fb 10.1109/TIE.2010.2055771 |
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| Keywords | vehicle headlight pairing Vehicle detection intelligent transportation system multiple object tracking |
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| SubjectTerms | Cameras Context intelligent transportation system multiple object tracking Roads Robustness Tracking Vehicle detection vehicle headlight pairing Vehicles |
| Title | Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights |
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