ABSTRACT
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