Human Pose Estimation Core Library

A library of functions for human pose estimation with event-driven cameras

Please cite:

@InProceedings{Goyal_2023_CVPR,
    author    = {Goyal, Gaurvi and Di Pietro, Franco and Carissimi, Nicolo and Glover, Arren and Bartolozzi, Chiara},
    title     = {MoveEnet: Online High-Frequency Human Pose Estimation With an Event Camera},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
    pages     = {4023-4032}
}

article: MoveEnet: Online High-Frequency Human Pose Estimation With an Event Camera

also for the eH3.6m dataset:

https://zenodo.org/record/7842598

and checkout other publications

Please contribute your event-driven HPE application and datasets to enable comparisons!

Core (C++)

Compile and link the core C++ library in your application to use the event-based human pose estimation functions including:

• joint detectors: OpenPose built upon greyscales formed from events
• joint velocity estimation @>500Hz
• asynchronous pose fusion of joint velocity and detection
• event representation methods to be compatible with convolutional neural networks.

PyCore

Importable python libraries for joint detection

• event-based movenet: MoveEnet built on PyTorch

Examples

Some example applications are available giving ideas on how to use the HPE-core libraries

• MoveEnet inference code in corresponding example here

Evaluation

Python scripts can be used to compare different detectors and velocity estimation combinations

Datasets and Conversion

Scripts to convert datasets into common formats to easily facilitate valid comparisons, and consistency

Authors

@arrenglover @nicolocarissimi @gaurvigoyal @francodipietro

Event-driven Perception for Robotics

See Also

@INPROCEEDINGS{9845526,
  author={Carissimi, Nicolò and Goyal, Gaurvi and Pietro, Franco Di and Bartolozzi, Chiara and Glover, Arren},
  booktitle={2022 8th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)}, 
  title={Unlocking Static Images for Training Event-driven Neural Networks}, 
  year={2022},
  pages={1-4},
  doi={10.1109/EBCCSP56922.2022.9845526}}