Efficient Vehicle Tracking and Classification for an Automated Traffic Surveillance System

A. Ambardekar, Mircea Nicolescu, and G. Bebis (USA)


Computer vision, object tracking, traffic surveillance, vehicle detection, vehicle tracking, and vehicle classification.


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. We 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. We 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 our traffic surveillance application. The object (vehicle) classification uses two new techniques − color contour based matching and gradient based matching. Our experiments on several real traffic video sequences demonstrate good results for our foreground object detection, tracking, vehicle detection and vehicle speed estimation approaches.

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