Robust Vehicle Counting with Severe Shadows and Occlusions
Résumé
We present a robust real-time vision-based system for vehicle tracking and categorization, developed for traffic flow surveillance. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. Experimental results based on four large datasets show that our method can count and classify vehicles with a high level of performance (more than 98%).
| Origine | Fichiers produits par l'(les) auteur(s) |
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