As digital cameras and powerful computers have become wide-spread, the number of applications using vision techniques has increased significantly. One such application that has received significant attention from the computer vision community is traffic surveillance. The authors propose a new traffic surveillance system that works without prior, explicit camera calibration, and has the ability to perform surveillance tasks in real time. Camera intrinsic parameters and its position with respect to the ground plane were derived using geometric primitives common to any traffic scene. The authors use optical flow and knowledge of camera parameters to detect the pose of a vehicle in the 3D world. This information is used in a model-based vehicle detection and classification technique employed by their traffic surveillance application. The object (vehicle) classification uses two new techniques - color contour based matching and gradient based matching. The authors' experiments on several real traffic video sequences demonstrate good results for their foreground object detection, tracking, vehicle detection and vehicle speed estimation approaches.
Efficient vehicle tracking and classification for an automated traffic surveillance system
2008
6 Seiten, 6 Bilder, 10 Quellen
Aufsatz (Konferenz)
Englisch
Automatic Traffic Surveillance System for Vehicle Tracking and Classification
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