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Evaluation of feature-based vehicle trajectory extraction algorithms

Vehicle trajectories are and can be used in various intelligent transportation systems applications including driver behavior modelling and safety. Video-based approaches have been used to extract a large number of non-cooperative trajectories. However, it is difficult to evaluate the accuracies of the resulting trajectories. An algorithm-specific simulation tool is developed to evaluate the feature-grouping algorithm. We introduce a Kalman smoothing model to estimate vehicle trajectories and compare it with our previous rescaling-based trajectory estimation algorithm using the simulation tool. A comparison with GPS (WAAS) on real video clip is also presented. Our evaluation shows that the feature-based algorithms provide more accurate trajectories than those by previous approaches including one for the NGSIM system.