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action-recognition-using-mhi-based-hu-moments-with-hmms

Action recognition from video streams is among the active research topics in computer vision. The challenge is on the identification of the actions robustly regardless of the variations imposed by appearances of actions performed by different people. The challenge increases when the data is gathered from an outdoor environment, i.e. background and illumination variations. This paper proposes a Hidden Markov Model (HMM) based approach to model actions using Hu moments that are computed using a modified Motion History Images (MHI). The experiments performed using Weizmann dataset show that the proposed method generates 99% classification accuracy, which is higher than many state of the art techniques that use MHI and HMMs separately, or at least comparable with them.

android-kotlin-fundamentals-starter-apps

android-kotlin-fundamentals-starter-apps

bci_gcn

Using Graph Convolutional Networks on a BCI dataset

broxflow

OpenCV-Python implementation of the Brox Optical Flow algorithm.

dasyud

dasyud.github.io

dynamichandgest-gcn

Using Graph Convolutional Neural Networks to detect hand gestures from videos present in the 20BN-Jester dataset.

gib-uva-erp-classification

Classifying EEG signals in the GIB-UVa-ERP BCI dataset

ipython-notebooks

This repository contains IPython notebooks that I have written.

matrix-appservice-irc

Node.js IRC bridge for Matrix

singnorabot

Signora Blush?

torch_clustering

A pure PyTorch implementation of kmeans and GMM with distributed clustering.