A mixed autoregressive hidden-markov-chain model applied to people's movements

A "mixed autoregressive hidden Markov model" (MAR-HMM) is proposed for modeling people's movements. MAR-HMM is equivalent to a special case of an autoregressive hidden Markov model (AR-HMM), which takes into account changes of people's internal properties. The number of parameters is thus reduced in the case of MAR-HMM. A dataset is applied to evaluate MAR-HMM in this study. The prediction rate of MAR-HMM is 56.8% and that of AR-HMM is 51.5%. It is therefore concluded that MAR-HMM is applicable to trajectory analysis of pedestrians.