Awesome

<br>This repository contains the code for unsupervised group estimation applied to the trajectory prediction models.

🧑‍🤝‍🧑 GP-Graph Architecture 🧑‍🤝‍🧑

• Learns to assign each pedestrian into the most likely behavior group in an unsupervised manner.
• Pedestrian group pooling&unpooling and group hierarchy graph for group behavior modeling.
• Group-level latent vector sampling strategy to share the latent vector between group members.

Model Training

Setup

Environment <br>All models were trained and tested on Ubuntu 20.04 with Python 3.7 and PyTorch 1.9.0 with CUDA 11.1.

Dataset <br>Preprocessed ETH and UCY datasets are included in this repository, under ./dataset/. The train/validation/test splits are the same as those fond in Social-GAN.

Baseline models <br>This repository supports the SGCN baseline trajectory predictor. We have included model source codes from their official GitHub in model_baseline.py

Train GP-Graph

To train our GPGraph-SGCN on the ETH and UCY datasets at once, we provide a bash script train.sh for a simplified execution.

./train.sh

We provide additional arguments for experiments:

./train.sh -t <experiment_tag> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>

# Examples
./train.sh -d "hotel" -i "1"
./train.sh -t onescene -d "hotel" -i "1"
./train.sh -t allinonegpu -d "eth hotel univ zara1 zara2" -i "0 0 0 0 0"

If you want to train the model with custom hyper-parameters, use train.py instead of the script file.

Model Evaluation

Pretrained Models

We have included pretrained models in the ./checkpoints/ folder.

Evaluate GP-Graph

You can use test.py to evaluate our GPGraph-SGCN model.

python test.py

📖 Citation

If you find this code useful for your research, please cite our trajectory prediction papers :)

💬 LMTrajectory (CVPR'24) 🗨️ | 1️⃣ SingularTrajectory (CVPR'24) 1️⃣ | 🌌 EigenTrajectory (ICCV'23) 🌌 | 🚩 Graph‑TERN (AAAI'23) 🚩 | 🧑‍🤝‍🧑 GP‑Graph (ECCV'22) 🧑‍🤝‍🧑 | 🎲 NPSN (CVPR'22) 🎲 | 🧶 DMRGCN (AAAI'21) 🧶

@inproceedings{bae2022gpgraph,
  title={Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction},
  author={Bae, Inhwan and Park, Jin-Hwi and Jeon, Hae-Gon},
  booktitle={Proceedings of the European Conference on Computer Vision},
  year={2022}
}

@inproceedings{bae2024lmtrajectory,
  title={Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction},
  author={Bae, Inhwan and Lee, Junoh and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

@inproceedings{bae2024singulartrajectory,
  title={SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model},
  author={Bae, Inhwan and Park, Young-Jae and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

@inproceedings{bae2023eigentrajectory,
  title={EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting},
  author={Bae, Inhwan and Oh, Jean and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}

@article{bae2023graphtern,
  title={A Set of Control Points Conditioned Pedestrian Trajectory Prediction},
  author={Bae, Inhwan and Jeon, Hae-Gon},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2023}
}

@inproceedings{bae2022npsn,
  title={Non-Probability Sampling Network for Stochastic Human Trajectory Prediction},
  author={Bae, Inhwan and Park, Jin-Hwi and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

@article{bae2021dmrgcn,
  title={Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction},
  author={Bae, Inhwan and Jeon, Hae-Gon},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2021}
}

Acknowledgement

Part of our code is borrowed from SGCN. We thank the authors for releasing their code and models.