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This paper presents a novel approach to estimating the 3D velocity of vehicles from video. Here we propose using a Bayesian Network to classify objects into pedestrians and different types of vehicles, using 2D features extracted from the video taken from a stationary camera. The classification allows us to estimate an approximate 3D model for the different classes. The height information is then used with the image co-ordinates of the object and the camera's perspective projection matrix to estimate the objects 3D world co-ordinates and hence its 3D velocity. Accurate velocity and acceleration estimates are both very useful parameters in traffic monitoring systems. We show results of highly accurate classification and measurement of vehicle's motion from real life traffic video streams.