A Filter for Visual Tracking Based on a Stochastic Model for Driver Behaviour
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
Text
Maybank et al. "A Filter for Visual Tracking Based on a Stochastic Model for Driver Behaviour." European Conference on Computer Vision, 1996. doi:10.1007/3-540-61123-1_168Markdown
[Maybank et al. "A Filter for Visual Tracking Based on a Stochastic Model for Driver Behaviour." European Conference on Computer Vision, 1996.](https://mlanthology.org/eccv/1996/maybank1996eccv-filter/) doi:10.1007/3-540-61123-1_168BibTeX
@inproceedings{maybank1996eccv-filter,
title = {{A Filter for Visual Tracking Based on a Stochastic Model for Driver Behaviour}},
author = {Maybank, Stephen J. and Worrall, Anthony D. and Sullivan, Geoffrey D.},
booktitle = {European Conference on Computer Vision},
year = {1996},
pages = {540-549},
doi = {10.1007/3-540-61123-1_168},
url = {https://mlanthology.org/eccv/1996/maybank1996eccv-filter/}
}