SRLSTM

States Refinement LSTM
This is the code for SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction. CVPR2019, together with Vanilla-LSTM and Social-LSTM models.

Environment

The code is tested on Ubuntu 16.04, Python 3.5.4, numpy 1.13.3, pytorch 1.0.1.post2.

Train

The Default settings are to train on ETH-univ. Data cache and models will be in the subdirectory "./savedata/0/".

python .../SRLSTM/train.py

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

The datasets are selected on arguments '--test_set'. Five datasets in ETH/UCY are corresponding to the value of [0,1,2,3,4].

This command is to train model for ETH-hotel and save cache files in '/Your/save/directory/1'.

python .../SRLSTM/train.py --test_set 1 --save_base_dir '/Your/save/directory'

You can set your model name by "--train_model" and model type by "--model".

Detailed arguments description is given in train.py.

Test

python .../SRLSTM/test.py --test_set X --load_model XXX

Test example models are given in ./savedata/X/testmodel/testmodel_XXX.tar
To test on UCY-univ, using

python .../SRLSTM/test.py --test_set 4 --load_model 324 --batch_around_ped 64

To test on your own models, use your train.py and change the arguments of '--phase', '--train_model','--load_model' to 'test','YourModelName','YourModelEpoch'.

Citation

If you find this code useful, please cite us as

@inproceedings{zhang2019srlstm,
  title={SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction},
  author={Zhang, Pu and Ouyang, Wanli and Zhang, Pengfei and Xue, Jianru and Zheng, Nanning},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}