README.md

Unsupervised Traffic Accident Detection in First-Person Videos

Yu Yao, Mingze Xu, Yuchen Wang, David Crandall and Ella Atkins

This repo contains the code for our paper on unsupervised traffic accident detection.

💥 The full code will be released upon the acceptance of our paper.

💥 So far we have released the pytorch implementation of our ICRA paper Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems, which is an important building block for the traffic accident detection. The original project repo is https://github.com/MoonBlvd/fvl-ICRA2019

Future Object Localization

To train the model, run:

python train_fol.py --load_config YOUR_CONFIG_FILE

To test the model, run:

python test_fol.py --load_config YOUR_CONFIG_FILE

An example of the config file can be found in config/fol_ego_train.yaml

evaluation result

Note that we have only evaluated the model performance with prediction horizon 0.5 seconds. We are working on proving the 1 second and 2 seconds results.

Model	pred horizon	FDE	ADE	FIOU
FOL + Ego pred	0.5 sec	10.9	6.6	0.95

Citation

If you found the repo is useful, please feel free to cite our papers:

@article{yao2018egocentric,
title={Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems},
author={Yao, Yu and Xu, Mingze and Choi, Chiho and Crandall, David J and Atkins, Ella M and Dariush, Behzad},
journal={arXiv preprint arXiv:1809.07408},
year={2018}
}



@article{yao2019unsupervised,
title={Unsupervised Traffic Accident Detection in First-Person Videos},
author={Yao, Yu and Xu, Mingze and Wang, Yuchen and Crandall, David J and Atkins, Ella M},
journal={arXiv preprint arXiv:1903.00618},
year={2019}
}