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.
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© 1996 Springer-Verlag Berlin Heidelberg
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Maybank, S.J., Worrall, A.D., Sullivan, G.D. (1996). A filter for visual tracking based on a stochastic model for driver behaviour. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61123-1_168
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DOI: https://doi.org/10.1007/3-540-61123-1_168
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