Automatic Differentiation Facilitates OF-Integration into Steering-Angle-Based Road Vehicle Tracking
Abstract
The interpretation of road traffic scenes recorded by a stationary video camera requires a sufficiently large field of view in order to capture non-trivial maneuvers. Vehicle images will thus be small. Their reliable tracking needs to combine edge-based approaches for precision with area-oriented approaches, for example based on a match between estimated and predicted Optic Flow fields, for stability. The approach reported here estimates the steering angle of vehicles to be tracked and compares this as well as a velocity estimate with actual measurements performed in a suitably equipped test vehicle recorded while crossing a road intersection. In addition, tracking results for numerous other vehicles recorded in the same scene have been assessed interactively and are reported. Computation of the Jacobi matrix for the Kalman-Filter is facilitated by a "dual number" representation for partial derivatives.
Cite
Text
Leuck and Nagel. "Automatic Differentiation Facilitates OF-Integration into Steering-Angle-Based Road Vehicle Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.784659Markdown
[Leuck and Nagel. "Automatic Differentiation Facilitates OF-Integration into Steering-Angle-Based Road Vehicle Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/leuck1999cvpr-automatic/) doi:10.1109/CVPR.1999.784659BibTeX
@inproceedings{leuck1999cvpr-automatic,
title = {{Automatic Differentiation Facilitates OF-Integration into Steering-Angle-Based Road Vehicle Tracking}},
author = {Leuck, Holger and Nagel, Hans-Hellmut},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1999},
pages = {2360-2365},
doi = {10.1109/CVPR.1999.784659},
url = {https://mlanthology.org/cvpr/1999/leuck1999cvpr-automatic/}
}