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SIT

  • 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}
}

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