Measuring object speed using stereo tracking

Stereo vision provides us the ability to perceive objects in 3D. By continuously tracking an object in stereo videos, the information about object motion can be acquired. In this paper, we propose a novel method which unifies the tracking and speed measuring within the Bayesian filtering framework and solves this problem using particle filtering approach. Vehicle speed measuring is studied as a specific example. Environment constraints are integrated into the tracking for reducing the uncertainty to achieve more robust and accurate results. Experiment results on both synthetic and real traffic sequences demonstrate the accuracy and effectiveness of our proposed method.