ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login

Human Motion Analysis Using Hidden Markov Model

Journal: International Journal of Science and Research (IJSR) (Vol.6, No. 7)

Publication Date:

Authors : ; ;

Page : 930-935

Keywords : Hidden Markov Model; Feature Vector; Binary Image; Symbol Sequence; Weizmann database;

Source : Downloadexternal Find it from : Google Scholarexternal


The Hidden Markov Model (HMM) is a popular statistical tool for modeling a wide range of time series data. In this paper, we have used a feature based bottom up approach with HMMs that is characterized by its learning capability and time-scale invariability. To apply HMM to our aim, one set of time-sequential image is transformed into an image feature vector sequence, and the sequence is converted into a symbol sequence by vector quantization. In learning human action categories, the parameters of the HMMs, one per category were optimized so as to best describe the training sequences from the category. To recognize an observed sequence, the HMMs best match sequence is chosen. The reorganization rate can be improved by increasing the number of people used to generate the training data.

Last modified: 2021-06-30 19:29:57