Human Motion Analysis Using Hidden Markov Model

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.