Approximating and Maximising the Likelihood for a General State-Space Model

2001 ◽  
pp. 159-175 ◽  
Author(s):  
Markus Hürzeler ◽  
Hans R. Künsch
Keyword(s):  
State Space ◽  
State Space Model ◽  
General State ◽  
Space Model ◽  
General State Space

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

2015 ◽  
Vol XL-4/W5 ◽  
pp. 49-53
Author(s):  
W. Nakanishi ◽  
T. Fuse ◽  
T. Ishikawa
Keyword(s):  
Parameter Estimation ◽  
State Space ◽  
State Space Model ◽  
Human Tracking ◽  
General State ◽  
Space Model ◽  
Person Recognition ◽  
Adaptive Parameter ◽  
Tracking Process ◽  
General State Space

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring 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 coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

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