FARSA: Fully Automated Roadway Safety Assessment

This repository contains the model files, solver definitions, and learned weights for the networks described in the following publication:

FARSA: Fully Automated Roadway Safety Assessment (Weilian Song, Scott Workman, Armin Hadzic, Xu Zhang, Eric Green, Mei Chen, Reginald Souleyrette, Nathan Jacobs), In IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.

@inproceedings{song2018farsa,
  author={Song, Weilian and Workman, Scott and Hadzic, Armin and Zhang, Xu and Green, Eric and Chen, Mei and Souleyrette, Reginald and Jacobs, Nathan},
  title={FARSA: Fully Automated Roadway Safety Assessment},
  booktitle={{IEEE Winter Conference on Applications of Computer Vision (WACV)}},
  year={2018}
}

Getting Started

Download the trained weights required for the inference here, and extract them at the root of the repo.

Run the inference script by running python infer.py, or pass in your own panoramic image this way:

python infer.py --img_path path_to_image

Note

Labels for three extra auxiliary tasks were used during training (presence of shoulder rumble strips, center rumble strips, and motorcycle facilities), however the label distributions for these three tasks are extremely skewed, therefore they are ignored in the paper.

License

This software is released under a creative commons license which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode

Contact

Weilian Song
weilian.song@uky.edu
University of Kentucky
http://cs.uky.edu/~wso226/

Nathan Jacobs
jacobs@cs.uky.edu
University of Kentucky
http://cs.uky.edu/~jacobs/