Paper Info
Title
Calibration-Free Probabilistic Image Sensor Model For Bayesian Search And Tracking
Abstract
This paper presents a calibration-free approach to modelling an image sensor in probabilistic manner for recursive Bayesian search and tracking (SaT), such that its information can be utilised interchangeably between 'search' and 'track' mode. A technique is developed to modify uncertainties from previously established deterministic model into probability density function (PDF). In this technique, the sensor parameters are redefined in probabilistic manner, and, are combined to achieve the resultant sensor model. This model is independent of image data, and hence, is able to provide consistent and reliable PDF of the target state in 'search' and 'track' modes. Using area coverage method and one-step look ahead as control strategies, the proposed modelling technique was compared with a conventional technique. The model derived from proposed technique provided better estimation of target state and comprehended limitations of image recognition system. This model, in comparison to a pre-calibrated model example, was shown to have higher efficiency and reliability.
Year
DOI
Venue
2008
10.1504/IJAAC.2008.022177
INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL
Keywords
Field
DocType
sensor model, image sensor, probabilistic model, recursive Bayesian search and tracking, mechanistic deconvolution
Data mining,Image sensor,Computer science,Bayesian search theory,Recursive Bayesian estimation,Look-ahead,Deterministic system,Statistical model,Probabilistic logic,Probability density function
Journal
Volume
Issue
ISSN
2
2-3
1740-7516
Citations 
PageRank 
References 
0
0.34
5
Authors
2
Name
Order
Citations
PageRank
Shen Hin Lim111.07
Tomonari Furukawa236444.93