Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
Summary
This is the implemtation code and proposed dataset(ASL-DVS) for the following paper. Please cite following paper if you use this code or dataset in your own work. The paper is available via: https://ieeexplore.ieee.org/abstract/document/9199543
MLA:
Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E., & Andreopoulos, Y. (2019). Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing. IEEE Transactions on Image Processing, 9084-9008, 2020
BibTex:
@article{bi2020graph,
title={Graph-Based Spatio-Temporal Feature Learning for Neuromorphic Vision Sensing},
author={Bi, Yin and Chadha, Aaron and Abbas, Alhabib and Bourtsoulatze, Eirina and Andreopoulos, Yiannis},
journal={IEEE Transactions on Image Processing},
volume={29},
pages={9084--9098},
year={2020},
publisher={IEEE}
}
Dataset: UCF101-DVS, HMDB-DVS, ASLAN-DVS
We release largest neuromorphic human activity datasets including UCF101-DVS, HMDB-DVS and ASLAN-DVS, and make them available to the research community at the link: https://www.dropbox.com/sh/ie75dn246cacf6n/AACoU-_zkGOAwj51lSCM0JhGa?dl=0
Code Implementation
Requirements:
Python 2.7
Pytorch 1.0.1.post2
pytorch_geometric 1.1.2
Preparations:
Training graphs are saved in '../traingraph' folder.
Testing graphs are saved in '../testgraph' folder.
Each sample should contains feature of nodes, edge, pseudo adresses and label.
Running examples:
cd code
python main.py # running file for RGCNN+Plain 3D
#The results can be found in the 'Results' folder.
Contact
For any questions or bug reports, please contact Yin Bi at yin.bi.16@ucl.ac.uk .