gg3dhar

Author: Zahra Gharaee (zahra.gharaee@liu.se)

Overview

This repository contains codes of a novel approach gg3dhar to categorize human actions collected by a Kinect sensor. The architecture is designed using layers of growing grid neural networks in a biologically inspired hierarchical cognitive framework. Three different datasets of actions are used to evaluate the performance of the system: MSRAction3D dataset, Florence 3D actions dataset and UTKinect-Action3D Dataset. Anyone interested in using gg3dhar architecture and/or any of its components, please cite the following article/arxiv:

  @article {gharaee2020csr} {
      author = {Zahra Gharaee},
        title = {Hierarchical growing grid networks for skeleton based action recognition},
        booktitle = {Cognitive Systems Research},
        year = {2020}
        page = {11--29}
        volume = {63}
        DOI = {10.1016/j.cogsys.2020.05.002}
      }
    }

Run experiment

Run main_HAR.py scripts applying the settings, which specify dataset and hyperparameters required for training or testing the system.