Vehicle detection, tracking and classification in urban traffic
Vehicle detection, tracking and classification in urban traffic
- Neue Suche nach: Chen, Zezhi
- Neue Suche nach: Ellis, Tim
- Neue Suche nach: Velastin, Sergio A
- Neue Suche nach: Chen, Zezhi
- Neue Suche nach: Ellis, Tim
- Neue Suche nach: Velastin, Sergio A
This paper presents a system for vehicle detection, tracking and classification from roadside CCTV. The system counts vehicles and separates them into four categories: car, van, bus and motorcycle (including bicycles). A new background Gaussian Mixture Model (GMM) and shadow removal method have been used to deal with sudden illumination changes and camera vibration. A Kalman filter tracks a vehicle to enable classification by majority voting over several consecutive frames, and a level set method has been used to refine the foreground blob. Extensive experiments with real world data have been undertaken to evaluate system performance. The best performance results from training a SVM (Support Vector Machine) using a combination of a vehicle silhouette and intensity-based pyramid HOG features extracted following background subtraction, classifying foreground blobs with majority voting. The evaluation results from the videos are encouraging: for a detection rate of 96.39%, the false positive rate is only 1.36% and false negative rate 4.97%. Even including challenging weather conditions, classification accuracy is 94.69%.
Dokumentinformationen
Vehicle detection, tracking and classification in urban traffic
2012-09-01
403500 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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