Pedestrian Crossing Action Prediction Benchmark

Benchmark for evaluating pedestrian action prediction algorithms that inlcude code for training, testing and evaluating baseline and state-of-the-art models for pedestrian action prediction on PIE and JAAD datasets.

Paper: I. Kotseruba, A. Rasouli, J.K. Tsotsos, Benchmark for evaluating pedestrian action prediction. WACV, 2021 (see citation information below).

Installation instructions

1. Download and extract PIE and JAAD datasets.

   Follow the instructions provided in https://github.com/aras62/PIE and https://github.com/ykotseruba/JAAD.

2. Download Python data interface.

   Copy pie_data.py and jaad_data.py from the corresponding repositories into PedestrianActionBenchmark directory.

3. Install docker (see instructions for Ubuntu 16.04 and Ubuntu 20.04).

4. Change permissions for scripts in docker folder:

   chmod +x docker/*.sh
   

5. Build docker image

   docker/build_docker.sh
   

   Optionally, you may set custom image name and/or tag using this command (e.g. to use two GPUs in parallel):

   docker/build_docker.sh -im <image_name> -t <tag>
   

Running instructions using Docker

Run container in interactive mode:

Set paths for PIE and JAAD datasets in docker/run_docker.sh (see comments in the script).

Then run:

docker/run_docker.sh

Train and test models

Use train_test.py script with config_file:

python train_test.py -c <config_file>

For example, to train PCPA model run:

python train_test.py -c config_files/PCPA.yaml

The script will automatially save the trained model weights, configuration file and evaluation results in models/<dataset>/<model_name>/<current_date>/ folder.

See comments in the configs_default.yaml and action_predict.py for parameter descriptions.

Model-specific YAML files contain experiment options exp_opts that overwrite options in configs_default.yaml.

Test saved model

To re-run test on the saved model use:

python test_model.py <saved_files_path>

For example:

python test_model.py models/jaad/PCPA/01Oct2020-07h21m33s/

Authors

• Iuliia Kotseruba
• Amir Rasouli

Please email [email protected] or [email protected] if you have any issues with running the code or using the data.

License

This project is licensed under the MIT License - see the LICENSE file for details

Citation

If you use the results, analysis or code for the models presented in the paper, please cite:

@inproceedings{kotseruba2021benchmark,
	title={{Benchmark for Evaluating Pedestrian Action Prediction}},
	author={Kotseruba, Iuliia and Rasouli, Amir and Tsotsos, John K},
	booktitle={Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV)},
	pages={1258--1268},
	year={2021}
}

If you use model implementations provided in the benchmark, please cite the corresponding papers

• ATGC [1]
• C3D [2]
• ConvLSTM [3]
• HierarchicalRNN [4]
• I3D [5]
• MultiRNN [6]
• PCPA [7]
• SFRNN [8]
• SingleRNN [9]
• StackedRNN [10]
• Two_Stream [11]

[1] Amir Rasouli, Iuliia Kotseruba, and John K Tsotsos. Are they going to cross? A benchmark dataset and baseline for pedestrian crosswalk behavior. ICCVW, 2017.

[2] Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani,and Manohar Paluri. Learning spatiotemporal features with 3D convolutional networks. ICCV, 2015.

[3] Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung,Wai-Kin Wong, and Wang-chun Woo. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. NeurIPS, 2015.

[4] Yong Du, Wei Wang, and Liang Wang. Hierarchical recurrent neural network for skeleton based action recognition. CVPR, 2015

[5] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? A new model and the kinetics dataset. CVPR, 2017.

[6] Apratim Bhattacharyya, Mario Fritz, and Bernt Schiele. Long-term on-board prediction of people in traffic scenes under uncertainty. CVPR, 2018.

[7] Iuliia Kotseruba, Amir Rasouli, and John K Tsotsos, Benchmark for evaluating pedestrian action prediction. WACV, 2021.

[8] Amir Rasouli, Iuliia Kotseruba, and John K Tsotsos. Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs. BMVC, 2019

[9] Iuliia Kotseruba, Amir Rasouli, and John K Tsotsos. Do They Want to Cross? Understanding Pedestrian Intention for Behavior Prediction. In IEEE Intelligent Vehicles Symposium (IV), 2020.

[10] Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vi-jayanarasimhan, Oriol Vinyals, Rajat Monga, and GeorgeToderici. Beyond short snippets: Deep networks for video classification. CVPR, 2015.

[11] Karen Simonyan and Andrew Zisserman. Two-stream convolutional networks for action recognition in videos. NeurIPS, 2014.