title: Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model.
abstract: In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.
authors.affiliation: Department of Biomedical Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, 446-701, Republic of Korea. ziauddin@khu.ac.kr
authors.forename: Md
authors.initials: M
authors.lastname: Uddin
authors.forename: J J
authors.initials: JJ
authors.lastname: Lee
authors.forename: T S
authors.initials: TS
authors.lastname: Kim
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D016002:Discriminant Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008390:Markov Chains; D008954:Models, Biological; D009043:Motor Activity / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D016138:Walking / Q000502:physiology*; D051598:Whole Body Imaging / Q000379:methods*