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| Content Provider | Springer Nature Link |
|---|---|
| Author | Messelodi, Stefa Modena, Carla Maria Zanin, Michele |
| Copyright Year | 2005 |
| Abstract | The paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences coming from pole-mounted video cameras at urban crossroads. The system uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing a robust background updating, and a feature-based tracking method. It is able to describe the path of each detected vehicle, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one for distinguishing between classes having similar models, like bicycles and motorcycles. The system is flexible with respect to the intersection geometry and the camera position. Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a 100,000-people town in northern Italy. |
| Starting Page | 17 |
| Ending Page | 31 |
| Page Count | 15 |
| File Format | |
| ISSN | 14337541 |
| Journal | Pattern Analysis & Applications |
| Volume Number | 8 |
| Issue Number | 1-2 |
| e-ISSN | 1433755X |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2005-07-02 |
| Publisher Place | London |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Traffic scene analysis Background updating Object tracking Model-based classification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Vision and Pattern Recognition |
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