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This study presents a detection and classification system for vehicles and pedestrians in urban traffic scenes. This aims to guide surveillance operators and reduce human resources for observing hundreds of cameras in urban traffic surveillance. The authors perform per frame vehicle detection and classification using 3D models on calibrated cameras. Motion silhouettes (from background estimation) are extracted and compared to a projected model silhouette to identify the ground plane position and class of vehicles and pedestrians. The system is evaluated with the reference i-LIDS datasets from the UK Home Office. Performance for varying numbers of classes for three different weather conditions and for different video input filters is evaluated. The full system including detection and classification achieves a recall of 87% at a precision of 85.5% outperforming similar systems in the literature. The i-LIDS dataset is available to other researchers to compare with our results. The authors conclude with an outlook to use local features for improving the classification and detection performance.