# projectSTAR

**Repository Path**: wen_fan/projectSTAR

## Basic Information

- **Project Name**: projectSTAR
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-04
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# STAR
Code for [Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction](https://arxiv.org/abs/2005.08514)

### Environment

The code is tested on GTX 1080Ti, Python 3.6.3, numpy 1.17.5, pytorch 1.1.0 and CUDA9.0.

### Train

The Default settings are to train on ETH-univ dataset. 

Data cache and models will be stored in the subdirectory "./output/eth/" by default. Notice that for this repo, we only provide implementation on GPU.

```
git clone https://github.com/Majiker/STAR.git
cd STAR
python trainval.py
```

Configuration files are also created after the first run, arguments could be modified through configuration files or command line. 
Priority: command line \> configuration files \> default values in script.

The datasets are selected on arguments '--test_set'. Five datasets in ETH/UCY including
[**eth, hotel, zara1, zara2, univ**].

### Example

This command is to train model for ETH-hotel and start test at epoch 10. For different dataset, change 'hotel' to other datasets named in the last section.

```
python trainval.py --test_set hotel --start_test 10
```

During training, the model for Best FDE on the corresponding test dataset would be record.

### Cite STAR

If you find this repo useful, please consider citing our paper
```bibtex
@inproceedings{
    YuMa2020Spatio,
    title={Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction},
    author={Cunjun Yu and Xiao Ma and Jiawei Ren and Haiyu Zhao and Shuai Yi},
    booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
    month = {August},
    year={2020}
}
```


### Reference

The code base heavily borrows from [SR-LSTM](https://github.com/zhangpur/SR-LSTM)