TrajNet++ : The Trajectory Forecasting Framework
PyTorch implementation of Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
TrajNet++ is a large scale interaction-centric trajectory forecasting benchmark comprising explicit agent-agent scenarios. Our framework provides proper indexing of trajectories by defining a hierarchy of trajectory categorization. In addition, we provide an extensive evaluation system to test the gathered methods for a fair comparison. In our evaluation, we go beyond the standard distance-based metrics and introduce novel metrics that measure the capability of a model to emulate pedestrian behavior in crowds. Finally, we provide code implementations of > 10 popular human trajectory forecasting baselines.
Data Setup
The detailed step-by-step procedure for setting up the TrajNet++ framework can be found here
Converting External Datasets
To convert external datasets into the TrajNet++ framework, refer to this guide
Training Models
LSTM
The training script and its help menu: python -m trajnetbaselines.lstm.trainer --help
Run Example
## Our Proposed D-LSTM python -m trajnetbaselines.lstm.trainer --type directional --augment ## Social LSTM python -m trajnetbaselines.lstm.trainer --type social --augment --n 16 --embedding_arch two_layer --layer_dims 1024
GAN
The training script and its help menu: python -m trajnetbaselines.sgan.trainer --help
Run Example
## Social GAN (L2 Loss + Adversarial Loss) python -m trajnetbaselines.sgan.trainer --type directional --augment ## Social GAN (Variety Loss only) python -m trajnetbaselines.sgan.trainer --type directional --augment --d_steps 0 --k 3
Evaluation
The evaluation script and its help menu: python -m evaluator.trajnet_evaluator --help
Run Example
## TrajNet++ evaluator (saves model predictions. Useful for submission to TrajNet++ benchmark) python -m evaluator.trajnet_evaluator --output OUTPUT_BLOCK/trajdata/lstm_directional_None.pkl --path <path_to_test_file> ## Fast Evaluator (does not save model predictions) python -m evaluator.fast_evaluator --output OUTPUT_BLOCK/trajdata/lstm_directional_None.pkl --path <path_to_test_file>
More details regarding TrajNet++ evaluator are provided here
Evaluation on datasplits is based on the following categorization
Results
Unimodal Comparison of interaction encoder designs on interacting trajectories of TrajNet++ real world dataset. Errors reported are ADE / FDE in meters, collisions in mean % (std. dev. %) across 5 independent runs. Our goal is to reduce collisions in model predictions without compromising distance-based metrics.
| Method | ADE/FDE | Collisions |
| LSTM | 0.60/1.30 | 13.6 (0.2) |
| S-LSTM | 0.53/1.14 | 6.7 (0.2) |
| S-Attn | 0.56/1.21 | 9.0 (0.3) |
| S-GAN | 0.64/1.40 | 6.9 (0.5) |
| D-LSTM (ours) | 0.56/1.22 | 5.4 (0.3) |
Interpreting Forecasting Models
Visualizations of the decision-making of social interaction modules using layer-wise relevance propagation (LRP). The darker the yellow circles, the more is the weight provided by the primary pedestrian (blue) to the corresponding neighbour (yellow). |
Code implementation for explaining trajectory forecasting models using LRP can be found here
Benchmarking Models
We host the Trajnet++ Challenge on AICrowd allowing researchers to objectively evaluate and benchmark trajectory forecasting models on interaction-centric data. We rely on the spirit of crowdsourcing, and encourage researchers to submit their sequences to our benchmark, so the quality of trajectory forecasting models can keep increasing in tackling more challenging scenarios.
Citation
If you find this code useful in your research then please cite
@article{Kothari2020HumanTF,
title={Human Trajectory Forecasting in Crowds: A Deep Learning Perspective},
author={Parth Kothari and S. Kreiss and Alexandre Alahi},
journal={ArXiv},
year={2020},
volume={abs/2007.03639}
}
What should I do to make the accuracy show up correctly?
Thank you for your reply.
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