A Human Activity Recognition System Using Hidden Markov Models with Generalized Discriminant Analysis on Enhanced Independent Component Features

1Md. Zia Uddin, 2*Deok-Hwan Kim, 3Tae-Seong Kim

1 Department of Computer Education, Sungkyunkwan University, Republic of Korea.

2School of Electronic Engineering, Inha University, Republic of Korea

3Department of Biomedical Engineering, Kyung Hee University, Republic of Korea

Abstract:  Human activity recognition from time-sequential video images is an active research area in various applications such as video surveillance and smart homes nowadays. This paper presents a novel approach of automatic human activity recognition based on Generalized Discriminant Analysis (GDA) on Enhanced Independent Component (EIC) features from binary silhouette information to be used with Hidden Markov Model (HMM) for training and recognition. The recognition performance using GDA on EIC features has been compared to other conventional approaches including Principle Component (PC), EIC, and Linear Discriminant Analysis (LDA) on PC features where the preliminary results show the superiority of the proposed approach.

Keywords:  Human activity recognition, EICA, GDA, and HMM.