Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction

The official repo for "Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction" (accepted by CVPR 2023)

Guangyi Chen*, Zhenhao Chen*, Shunxing Fan, Kun Zhang **

Abstract

The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite samples, due to the long tail effect of the predicted distribution. To promote the sampling process of stochastic prediction, we propose a novel method, called BOsampler , to adaptively mine potential paths with Bayesian optimization in an unsupervised manner, as a sequential design strategy in which new prediction is dependent on the previously drawn samples. Specifically, we model the trajectory sampling as a Gaussian process and construct an acquisition function to measure the potential sampling value. This acquisition function applies the original distribution as prior and encourages exploring paths in the long-tail region. This sampling method can be integrated with existing stochastic predictive models without retraining. Experimental results on various baseline methods demonstrate the effectiveness of our method.

* Authors contributed equally and are listed alphabetically by first name

** Code & Configuration on the exception subset will be updated later.

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Introduction

BOsampler is a plug-and-play method to promote the sampling process of stochastic prediction in human trajectory forecasting without retraining the model or accessing the source data.
BOsampler adaptsively mines potential paths with Bayesian optimization in an unsupervised manner, as a sequential design strategy in which new predictions depend on the previously drawn samples.
The acquisition function encourages exploring paths in the long-tail region of the distribution and achieving a trade-off between accuracy and diversity.
BOsampler can be directly integrated with existing stochastic predictive models without retraining.
Compared to Monte Carlo and Quasi-Monte Carlo sampling methods, BOsampler can encourage exploration of low-probability choices and improve the diversity of samples.

The figure above is an illustration of how our method generate new samples in an iterative manner. Given w existing samples, we first characterize two functions: $\mu_w$ and $\sqrt{\beta \kappa_w}$. Then, we use these two functions to fit a Gaussian Process and calculate the posterior distribution. Next, we use the posterior distribution to calculate the acquisition function $\phi(\text{z}_w)$. Then, the next sample is generated by maximizing $\phi(\text{z}_w)$.Finally, we add this new sample to inputs and can use it to update the distribution.

Requirements

We prepare the implementation of BOsampler on PECnet baseline as an example. It is easy to extend our code to other baseline models, too. The default settings has been verified to run on NVIDIA A6000 (48G VRAM) and NVIDIA RTX 3090 (24G VRAM). To run on lower-end GPUs, you might have to reduce the number of batch_size in ./configs/pecnet/bo.yaml

PyTorch==1.11.0
BoTorch==0.6.4
numpy,easydict,pyyaml

Prepare the running environment using conda (Recommended)

We recommend using conda to create a virtual environment to run BOsampler.

First, create a new conda environment.

conda create -n bosampler python=3.8

Activate the conda environment just created.

conda activate bosampler

Install PyTorch using official channel.

conda install pytorch==1.11.0 cudatoolkit=11.3 -c pytorch

Use pip to install other requirements.

pip install easydict pyyaml numpy botorch==0.6.4

How to test

We prepare three shell scripts for testing. run_mc.sh for Monte Carlo method, run_qmc.sh for Quasi-Monte Carlo method, and run_bo.sh for BOsampler method. You can use a unix shell like /bin/sh to exexute any of those scripts as you like.

Run BOsampler

sh run_bo.sh

Adjust for other hyperparameter configurations

We store the configuration files under ./configs. You can explore other settings as you like by editing those configuration files.

BibTex

If you find our work helpful, please cite us by adding the following BibTex.

@inproceedings{chen2023unsupervised,
  title={Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction},
  author={Chen, Guangyi and Chen, Zhenhao and Fan, Shunxing and Zhang, Kun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={17874--17884},
  year={2023}
}

Acknowledgement

This project was partially supported by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, by a grant from Apple Inc., a grant from KDDI Research Inc., and generous gifts from Salesforce Inc., Microsoft Research, and Amazon Research.

We would like to thank our colleague Zunhao Zhang from MBZUAI for providing computation resource for part of the experiments.

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