Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Yu Yao, Mingze Xu, Chiho Choi, David J. Crandall, Ella M. Atkins, and Behzad Dariush
Introduction
This repo contains the source code of future vehicle localization (FVL)[1], implemented in Keras with tensorflow backend.
Following dependencies (or newer version):
python3.5 or python3.6
tensorflow-gpu=1.1.0
keras=1.1.0.
The RNN encoder-decoder model:
To train the model, run
cd train
sh run_train.sh
Check the command line arguments by python train.py --help
To test the trained model, run
cd test
sh run_test.sh
We tested our model
Test results:
Test results on HEV-I dataset:
| Models | Easy Cases | Challenging Cases | All Cases |
|---|---|---|---|
| Linear | 31.49 / 17.04 / 0.68 | 107.93 / 56.29 / 0.33 | 72.37 / 38.04 / 0.50 |
| ConstAccel | 20.82 / 13.86 / 0.74 | 90.33 / 49.06 / 0.35 | 58.00 / 28.05 / 0.53 |
| Conv1D [2] | 18.84 / 12.09 / 0.75 | 37.95 / 20.97 / 0.64 | 29.06 / 16.84 / 0.69 |
| RNN-ED-X | 23.57 / 11.96 / 0.74 | 43.15 / 22.24 / 0.60 | 34.04 / 17.46 / 0.67 |
| RNN-ED-XE | 22.28 / 11.60 / 0.74 | 42.27 / 22.39 / 0.61 | 32.97 / 17.37 / 0.67 |
| RNN-ED-XO | 17.45 / 8.68 / 0.78 | 32.61 / 16.72 / 0.66 | 25.56 / 12.98 / 0.72 |
| RNN-ED-XOE | 16.72 / 8.52 / 0.80 | 32.05 / 16.63 / 0.66 | 24.92 / 12.86 / 0.73 |
Test results on KITTI dataset:
| Models | FDE | ADE | FIOU |
|---|---|---|---|
| Linear | 78.19 | 38.21 | 0.33 |
| ConstAccel | 55.66 | 25.78 | 0.39 |
| Conv1D [2] | 44.13 | 24.38 | 0.49 |
| Ours | 37.11 | 17.88 | 0.53 |
Dataset Demo
Prediction Demo
Citation
If you found the repo is useful, please feel free to cite our paper:
@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}
}
Reference
[1] Yao Y, Xu M, Choi C, Crandall DJ, Atkins EM, Dariush B. Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems. arXiv preprint arXiv:1809.07408. 2018 Sep 19.
[2] Yagi T, Mangalam K, Yonetani R, Sato Y. Future person localization in first-person videos. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018 (pp. 7593-7602).