Awesome

<br>This repository contains the code for control point conditioned prediction and the initial trajectory refinement network for trajectory prediction.

🚩 Graph-TERN Model 🚩

• Control points divide each pedestrian's future path into sections and are inferred by each stochastic goal.
• Gaussian mixture model pruning scheme to effectively cutting-off abnormal behaviors of pedestrians.
• Initial trajectory refinement by adding the correction vector field to the linearly interpolated path.

News

• The latest version of Graph-TERN is now available, offering significantly improved results!
• You can find the exact AAAI'23 version here.

Model Training

Setup

Environment <br>All models were trained and tested on Ubuntu 18.04 with Python 3.7 and PyTorch 1.6.0 with CUDA 10.1.

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.

Train Graph-TERN

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

./scripts/train.sh

We provide additional arguments for experiments:

./scripts/train.sh -p <experiment_tag_prefix> -s <experiment_tag_suffix> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>

# Examples
./scripts/train.sh -d "hotel" -i "1"
./scripts/train.sh -p graph-tern_ -s _experiment -d "zara2" -i "2"
./scripts/train.sh -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.

python train.py --input_size <input_coordinate_dimension> --output_size <output_gaussian_dimension> \
--n_epgcn <number_of_control_point_gcn_layers> --n_epcnn <number_of_control_point_cnn_layers> \
--n_trgcn <number_of_refinement_gcn_layers> --n_trcnn <number_of_refinement_cnn_layers> \
--n_ways <number_of_control_points>  --n_smpl <number_of_samples_for_refine> --kernel_size <kernel_size>\
--obs_seq_len <observation_length> --pred_seq_len  <prediction_length> --dataset <dataset_name> \
--batch_size <minibatch_size> --num_epochs <number_of_epochs> --clip_grad <gradient_clipping> \
--lr <learning_rate> --lr_sh_rate <number_of_steps_to_drop_lr> --use_lrschd <use_lr_scheduler> \
--tag <experiment_tag>

Model Evaluation

Pretrained Models

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

Evaluate Graph-TERN

To evaluate our Graph-TERN at once, we provide a bash script test.sh for a simplified execution.

./scripts/test.sh -p <experiment_tag_prefix> -s <experiment_tag_suffix> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>

# Examples
./scripts/test.sh
./scripts/test.sh -d "hotel" -i "1"
./scripts/test.sh -p graph-tern_ -s _experiment -d "zara2" -i "2"
./scripts/test.sh -d "eth hotel univ zara1 zara2" -i "0 0 0 0 0"

If you want to evaluate the model individually, you can use test.py with custom hyper-parameters.

python test.py --tag <experiment_tag> --n_samples <number_of_multimodal_samples>

# Examples
python test.py --tag graph-tern_eth_experiment
python test.py --tag graph-tern_hotel_experiment
python test.py --tag graph-tern_univ_experiment
python test.py --tag graph-tern_zara1_experiment
python test.py --tag graph-tern_zara2_experiment

📖 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) 🧶

@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{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}
}

@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{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}
}