MKIM1008 / Social-STGCNN

Code for "Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction" CVPR 2020

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

Abduallah Mohamed, Kun Qian

Mohamed Elhoseiny** , Christian Claudel**

^** Equal advising

Read full paper here, Presented at CVPR 2020

Social-STGCNN

We propose the Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN), which models the problem of human trajectory prediction as a spatio-temporal graph. Our results show an improvement over the state of art by 20% on the Final Displacement Error (FDE) and an improvement on the Average Displacement Error (ADE) with 8.5 times less parameters and up to 48 times faster inference speed than previously reported methods. In addition, our model is data efficient, and exceeds previous state of the art on the ADE metric with only 20% of the training data. We propose a kernel function to embed the social interactions between pedestrians within the adjacency matrix.

Our model inference speed is 0.002s/frame (500Hz) using only 7.6K parameters.

Citaion

You can cite our paper using:

@inproceedings{mohamed2020social,
  title={Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction},
  author={Mohamed, Abduallah and Qian, Kun and Elhoseiny, Mohamed and Claudel, Christian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14424--14432},
  year={2020}
}

Model

Social-STGCNN model consists of 2 building blocks:
1- ST-GCNN: A Spatio-Tempral Graph CNN that creates a spatio-temporal graph embedding representing the previous pedestrians trajectories.
2- TXP-CNN: A Time-Extrapolator CNN that utilizes the spatio-temporal graph embedding to predict future trajectories.

More details are in this description video:

Predictions sample

Setup:

The code was written using python 3.6. The following libraries are the minimal to run the code:

import pytorch
import networkx
import numpy
import tqdm

or you can have everything set up by running:

pip install -r requirements.txt

Using the code:

To use the pretrained models at checkpoint/ and evaluate the models performance run:

test.py

To train a model for each data set with the best configuration as in the paper, simply run:

./train.sh  

Please note: The initiation of training and testing might take a while as the code creates a cache to store the spatio-temporal graphs.
Exact CVPR version: https://github.com/abduallahmohamed/Social-STGCNN/tree/ebd57aaf34d84763825d05cf9d4eff738d8c96bb

Check our work in the 3D mode: https://github.com/abduallahmohamed/Skeleton-Graph