Kalman Filtering Used in Video-Based Traffic Monitoring System
Video object tracking is an important method of traffic detection in Intelligent Transportation Systems. In video traffic tracking systems the matching method is often used to find the position of moving objects. In this article an improved algorithm of corner feature extraction is presented and cor...
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| Published in | Journal of intelligent transportation systems Vol. 10; no. 1; pp. 15 - 21 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis Group
01.01.2006
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1547-2450 1547-2442 |
| DOI | 10.1080/15472450500455211 |
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| Abstract | Video object tracking is an important method of traffic detection in Intelligent Transportation Systems. In video traffic tracking systems the matching method is often used to find the position of moving objects. In this article an improved algorithm of corner feature extraction is presented and corner points are tracked as the feature points of traffic objects. The tracking precision is mainly decided by matching algorithms. If the matching is not accurate, good tracking results cannot be achieved. In this article Kalman Filtering is used to track the moving traffic objects. In this system two kinds of data are used: One is from the general matching algorithm, which is the representation of the target's position; the other is detected by a spatial filtering velocimeter, containing the rough flow velocity of the targets. Though neither kind of data are highly accurate, Kalman Filtering is capable of integrating both position and velocity data to obtain better tracking results. |
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| AbstractList | Video object tracking is an important method of traffic detection in Intelligent Transportation Systems. In video traffic tracking systems the matching method is often used to find the position of moving objects. In this article an improved algorithm of corner feature extraction is presented and corner points are tracked as the feature points of traffic objects. The tracking precision is mainly decided by matching algorithms. If the matching is not accurate, good tracking results cannot be achieved. In this article Kalman Filtering is used to track the moving traffic objects. In this system two kinds of data are used: One is from the general matching algorithm, which is the representation of the target's position; the other is detected by a spatial filtering velocimeter, containing the rough flow velocity of the targets. Though neither kind of data are highly accurate, Kalman Filtering is capable of integrating both position and velocity data to obtain better tracking results. |
| Author | Yao, Danya Qiu, Zhijun |
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| Cites_doi | 10.1109/25.69968 10.1016/S0968-090X(98)00019-9 10.1109/5254.735999 10.1016/S0167-8655(98)00134-2 10.1109/MNRAO.1994.346236 |
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| References | Qiu Z. J. (CIT0009) Benjamin C. (CIT0002) 1998; 6 Zheng Z. (CIT0012) 1999; 20 CIT0001 Stauffer C. (CIT0010) 2000 Chabat F. (CIT0004) 1999 Bozic S. M. (CIT0003) 1979 Masaki I. (CIT0006) 1998; 13 Grewal M. S. (CIT0005) 1993 CIT0007 Qiu Z. J. (CIT0008) Yao D. Y. (CIT0011) |
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| Snippet | Video object tracking is an important method of traffic detection in Intelligent Transportation Systems. In video traffic tracking systems the matching method... |
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| SubjectTerms | Corner Detection Kalman Filtering Position Matching Spatial Filtering |
| Title | Kalman Filtering Used in Video-Based Traffic Monitoring System |
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