NEW: Converting new external dataset into TrajNet++ format. Tutorial

Install

pip install -e '.[test,plot]'
sh setup_orca.sh
sh setup_social_force.sh

Prepare Synthetic Dataset

python -m trajnetdataset.controlled_data --mode 'trajnet' --num_scenes 1000

Converting Synthetic Dataset to TrajNet++

A command to categorize synthetic data into TrajNet++ format will be printed at the end of preparation command above.

Preparing Real World Data

Existing real world data:

data/
    data_arxiepiskopi.rar
    data_university_students.rar
    data_zara.rar
    ewap_dataset_light.tgz
    # 3DMOT2015Labels  # from: https://motchallenge.net/data/3DMOT2015Labels.zip (video file at http://cs.binghamton.edu/~mrldata/public/PETS2009/S2_L1.tar.bz2)
    Train.zip  # from trajnet.epfl.ch
    cvpr2015_pedestrianWalkingPathDataset.rar  # from http://www.ee.cuhk.edu.hk/~syi/ (website not accessible but data are also here: https://www.dropbox.com/s/7y90xsxq0l0yv8d/cvpr2015_pedestrianWalkingPathDataset.rar?dl=0.+63)
    cff_dataset.zip # from https://www.dropbox.com/s/cnnk2ofreeoshuz/cff_dataset.zip?dl=0

Extract:

# biwi
mkdir -p data/raw/biwi
tar -xzf data/ewap_dataset_light.tgz --strip-components=1 -C data/raw/biwi

# crowds
mkdir -p data/raw/crowds
unrar e data/data_arxiepiskopi.rar data/raw/crowds
unrar e data/data_university_students.rar data/raw/crowds
unrar e data/data_zara.rar data/raw/crowds

# cff
mkdir -p data/raw/cff_dataset
unzip data/cff_dataset.zip -d data/raw/
rm -r data/raw/__MACOSX

# Wildtrack: https://www.epfl.ch/labs/cvlab/data/data-wildtrack/
mkdir -p data/raw/wildtrack
unzip data/Wildtrack_dataset_full.zip -d data/raw/wildtrack

# L-CAS: https://drive.google.com/drive/folders/1CPV9XeJsZzvtTxPQ9u1ppLGs_29e-XdQ
mkdir -p data/raw/lcas
cp data/lcas_pedestrian_dataset/minerva/train/data.csv data/raw/lcas

# pedestrian walking dataset
mkdir -p data/raw/syi
unrar e data/cvpr2015_pedestrianWalkingPathDataset.rar data/raw/syi

PETS09 S2L1 ground truth -- not used because people behavior is not normal
mkdir -p data/raw/mot
unzip data/3DMOT2015Labels.zip -d data/
cp data/3DMOT2015Labels/train/PETS09-S2L1/gt/gt.txt data/raw/mot/pets2009_s2l1.txt

# Edinburgh Informatics Forum tracker -- not used because tracks are not good enough
mkdir -p data/raw/edinburgh
wget -i edinburgh_informatics_forum_urls.txt -P data/raw/edinburgh/

Converting Real World Dataset

python -m trajnetdataset.convert

The above command performs the following operations:

• Step 1. readers.py: reads the raw data files and converts them to trackrows in .ndjson format
• Step 2. scene.py: prepares different scenes given the obtained trackrows
• Step 3. get_type.py: categorizes each scene based on our defined trajectory categorization

# create plots to check new dataset
python -m trajnetplusplustools.summarize output/train/*.ndjson

# obtain new dataset statistics
python -m trajnetplusplustools.dataset_stats output/train/*.ndjson

# visualize sample scenes
python -m trajnetplusplustools.trajectories output/train/*.ndjson

Converting Other Real World Datasets

Refer to this example tutorial: Tutorial

More Synthetic Toy Dataset Examples

Checkout the toy branch

Difference in generated data in TrajNet++

• partial tracks are now included (for correct occupancy maps)
• pedestrians that appear in multiple chunks had the same id before (might be a problem for some input readers)
• explicit index of scenes with annotation of the primary pedestrian

# * the primary pedestrian has to move by more than 1 meter * at one point, the primary pedestrian has to be <3m away from another pedestrian

Citation

If you find this code useful in your research then please cite

@article{Kothari2020HumanTF,
  author={Kothari, Parth and Kreiss, Sven and Alahi, Alexandre},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  title={Human Trajectory Forecasting in Crowds: A Deep Learning Perspective},
  year={2021},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TITS.2021.3069362}
 }

References

• eth:

@article{Pellegrini2009YoullNW,
  title={You'll never walk alone: Modeling social behavior for multi-target tracking},
  author={Stefano Pellegrini and Andreas Ess and Konrad Schindler and Luc Van Gool},
  journal={2009 IEEE 12th International Conference on Computer Vision},
  year={2009},
  pages={261-268}
}

• ucy:

@article{Lerner2007CrowdsBE,
  title={Crowds by Example},
  author={Alon Lerner and Yiorgos Chrysanthou and Dani Lischinski},
  journal={Comput. Graph. Forum},
  year={2007},
  volume={26},
  pages={655-664}
}

• wildtrack:

@inproceedings{chavdarova-et-al-2018,
    author = "Chavdarova, T. and Baqué, P. and Bouquet, S. and Maksai, A. and Jose, C. and Bagautdinov, T. and Lettry, L. and Fua, P. and Van Gool, L. and Fleuret, F.",
    title = {{WILDTRACK}: A Multi-camera {HD} Dataset for Dense Unscripted Pedestrian Detection},
    journal = "Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR)",
    year = 2018,
}

• L-CAS:

@article{Sun20173DOFPT,
  title={3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data},
  author={Li Sun and Zhi Yan and Sergi Molina Mellado and Marc Hanheide and Tom Duckett},
  journal={2018 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2017},
  pages={1-7}
}

• CFF:

@article{Alahi2014SociallyAwareLC,
    title={Socially-Aware Large-Scale Crowd Forecasting},
    author={Alexandre Alahi and Vignesh Ramanathan and Fei-Fei Li},
    journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
    year={2014},
    pages={2211-2218}
  }

• syi: Shuai Yi, Hongsheng Li, and Xiaogang Wang. Understanding Pedestrian Behaviors from Stationary Crowd Groups. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015).
• edinburgh: B. Majecka, "Statistical models of pedestrian behaviour in the Forum", MSc Dissertation, School of Informatics, University of Edinburgh, 2009.