Combining Multiple Motion Estimates for Vehicle Tracking
Abstract
In this paper, the problem of combining estimates provided by multiple models is considered, with application to vehicle tracking. Two tracking systems, based on the bounding-box and on the 2-D pattern of the targets, provide individual motion parameters estimates to the combining method, which in turn produces a global estimate. Two methods are proposed to combine the estimates of these tracking systems: one is based on their covariance matrix, while the other one employs a Kalman filter model. Results are provided on three image sequences taken under different viewpoints, weather conditions and varying vehicle/road contrasts. Two evaluations are made. First, the performances of individual and global estimates are compared. Second, the two global estimates are compared and the superiority of the second method is assessed over the first one.
Cite
Text
Gil et al. "Combining Multiple Motion Estimates for Vehicle Tracking." European Conference on Computer Vision, 1996. doi:10.1007/3-540-61123-1_149Markdown
[Gil et al. "Combining Multiple Motion Estimates for Vehicle Tracking." European Conference on Computer Vision, 1996.](https://mlanthology.org/eccv/1996/gil1996eccv-combining/) doi:10.1007/3-540-61123-1_149BibTeX
@inproceedings{gil1996eccv-combining,
title = {{Combining Multiple Motion Estimates for Vehicle Tracking}},
author = {Gil, Sylvia and Milanese, Ruggero and Pun, Thierry},
booktitle = {European Conference on Computer Vision},
year = {1996},
pages = {307-320},
doi = {10.1007/3-540-61123-1_149},
url = {https://mlanthology.org/eccv/1996/gil1996eccv-combining/}
}