Setup
Install miniconda, then create the environment and activate it via
conda env create -f environment.yml
conda activate ai_cityEvaluate
As a zero-shot system, no training is required. We use Mask R-CNN pretrained on COCO from detectron2 as detector, whose weights will be downloaded automatically at the first run.
As the dataset only provided screenshots of the pre-defined routes, we created our own annotation of them with labelme.
To get system outputs, run
python run.py **args
Performance
Visualizations available at Google Drive.
Reference
@inproceedings{yu2020zero,
title={Zero-VIRUS: Zero-shot VehIcle Route Understanding System for Intelligent Transportation},
author={Yu, Lijun and Feng, Qianyu and Qian, Yijun and Liu, Wenhe and Hauptmann, Alexander G.},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
year={2020}
}