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This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In manyhuman tracking method, any parameters of models used for recognize the same person in successive frames are usually set inadvance of human tracking process. In real situation these parameters may change according to situation of observation anddifficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general statespace model. Firstly we explain the way to formulate human tracking in general state space model with their components. Thenreferring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model,which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficientwith one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed thatsequential parameter estimation was succeeded and were consistent with observation situations such as occlusions

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

ADAPTIVE PARAMETER ESTIMATION OF PERSON RECOGNITION MODEL IN A STOCHASTIC HUMAN TRACKING PROCESS