Abstract
A driver controls a car by turning the steering wheel or by pressing on the accelerator or the brake. These actions are modelled by Gaussian processes, leading to a stochastic model for the motion of the car. The stochastic model is the basis of a new filter for tracking and predicting the motion of the car, using measurements obtained by fitting a rigid 3D model to a monocular sequence of video images. Experiments show that the filter easily outperforms traditional filters.
Cite
CITATION STYLE
Maybank, S. J., Worrail, A. D., & Sullivan, G. D. (1996). A filter for visual tracking based on a stochastic model for driver behaviour. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1065, pp. 540–549). Springer Verlag. https://doi.org/10.1007/3-540-61123-1_168
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