Pedestrian_graph_plus
This is a code repo for Pedestrian Graph +: A Fast Pedestrian Crossing Prediction Model based on Graph Convolutional Networks
Google colab
- Pedestrian Graph + Available via a colab notebook .
wath Pedestrian Graph + on:
bilibili
Or on Youtube
BibTeX
If you use any of this code, please cite the following publications:
@ARTICLE{9774877,
author={Cadena, Pablo Rodrigo Gantier and Qian, Yeqiang and Wang, Chunxiang and Yang, Ming},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={Pedestrian Graph +: A Fast Pedestrian Crossing Prediction Model Based on Graph Convolutional Networks},
year={2022},
volume={},
number={},
pages={1-12},
doi={10.1109/TITS.2022.3173537}}
@inproceedings{cadena2019pedestrian,
title={Pedestrian graph: Pedestrian crossing prediction based on 2d pose estimation and graph convolutional networks},
author={Cadena, Pablo Rodrigo Gantier and Yang, Ming and Qian, Yeqiang and Wang, Chunxiang},
booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)},
pages={2000--2005},
year={2019},
organization={IEEE}
}
Set-up
install:
pytorch 1.8.0 or above
pytorch lightning 1.5.10 or above
Also you can use docker
sudo docker pull nvcr.io/nvidia/l4t-base:r32.4.3
sudo run sh ./run_docker.sh
our code was tested on the jetson nano 4Gb
Preliminary
- Download the linked material below
Sample dataset for training and testing:
pre-processed data baidu (data).
pre-processed data google dive (data).
PIE data baidu (PIE).
PIE data google dive (PIE).
JAAD data baidu (JAAD).
JAAD data google dive (JAAD).
All three folders must be inside the Pedestrian_graph_plus folder
Inference
test JAAD
python3 final_jaad_test.py --ckpt ./weigths/jaad-23-IVSFT/best.pth
The following inference is made on a 4Gb jetson nano:
This inference shows that Pedestrian Graph + is able to run on low-resource hardware, being efficient while maintaining high accuracy.
Inference time on jetson nano is 24ms, on the GTX 1060 (laptop) the inference time is 3 ms.
test JAAD with 2D human keypoints
python3 final_jaad_test.py --ckpt ./weigths/jaad-23-IVSFT-h2d/best.pth
test PIE
python3 final_pie_test.py --ckpt ./weigths/jaad-23-IVSFT-h2d/best.pth
To train
python3 pl_jaad_muster23_forecast.py --logdir ./weigths/jaad-23-IVSFT/
Qualitative Results
License
MIT license