lssiair / SIT

Social Interpretable Tree for Pedestrian Trajectory Prediction (AAAI22)

SIT

Paper

• This is the official implementation of the paper: Social Interpretable Tree for Pedestrian Trajectory Prediction (AAAI 2022).

Quick Start

Requires:

• Python== 3.6
• numpy==1.16.4
• torch==1.4.0

1) Install Packages

 pip install -r requirements.txt

2) Dataset

Please download the dataset and extract it into the directory './dataset/' like this:

./dataset/train/
./dataset/test/

Performance

Results on ETH-UCY and Stanford Drone Dataset:

	minADE	minFDE
ETH	0.39	0.62
HOTEL	0.14	0.22
UNIV	0.27	0.47
ZARA1	0.19	0.33
ZARA2	0.16	0.29
AVG ETH-UCY	0.23	0.38
SDD	9.13	15.42

SIT

Training & Evaluation

Suppose the training data is at ./dataset/. You can train and evaluate our model on the 'eth' dataset by below command:

bash train.sh 'eth'

Training on a single 2080Ti.

Acknowledgement

Thank for the pre-processed data provided by the works of PECNet.

Citation

If you find our work useful for your research, please consider citing the paper:

@inproceedings{sit,
  title={Social Interpretable Tree for Pedestrian Trajectory Prediction},
  author={Shi, Liushuai and Wang, Le and Long, Chengjiang and Zhou, Sanping and Zheng, Fang and Zheng, Nanning  and Hua, Gang},
  booktitle={Association for the Advance of Artificial Intelligence},
  year={2022}
}