TY - GEN
T1 - Bayesian fusion of thermal and visible spectra camera data for region based tracking with rapid background adaptation
AU - Stolkin, Rustam
AU - Rees, David
AU - Talha, Mohammed
AU - Florescu, Ionut
PY - 2012
Y1 - 2012
N2 - This paper presents a method for optimally combining pixel information from an infra-red thermal imaging camera, and a conventional visible spectrum colour camera, for tracking a moving target. The tracking algorithm rapidly re-learns its background models for each camera modality from scratch at every frame. This enables, firstly, automatic adjustment of the relative importance of thermal and visible information in decision making, and, secondly, a degree of camouflage target tracking by continuously re-weighting the importance of those parts of the target model that are most distinct from the present background at each frame. Furthermore, this very rapid background adaptation ensures robustness to large, sudden and arbitrary camera motion, and thus makes this method a useful tool for robotics, for example visual servoing of a pan-tilt turret mounted on a moving robot vehicle. The method can be used to track any kind of arbitrarily shaped or deforming object, however the combination of thermal and visible information proves particularly useful for enabling robots to track people. The method is also important in that it can be readily extended for data fusion of an arbitrary number of statistically independent features from one or arbitrarily many imaging modalities.
AB - This paper presents a method for optimally combining pixel information from an infra-red thermal imaging camera, and a conventional visible spectrum colour camera, for tracking a moving target. The tracking algorithm rapidly re-learns its background models for each camera modality from scratch at every frame. This enables, firstly, automatic adjustment of the relative importance of thermal and visible information in decision making, and, secondly, a degree of camouflage target tracking by continuously re-weighting the importance of those parts of the target model that are most distinct from the present background at each frame. Furthermore, this very rapid background adaptation ensures robustness to large, sudden and arbitrary camera motion, and thus makes this method a useful tool for robotics, for example visual servoing of a pan-tilt turret mounted on a moving robot vehicle. The method can be used to track any kind of arbitrarily shaped or deforming object, however the combination of thermal and visible information proves particularly useful for enabling robots to track people. The method is also important in that it can be readily extended for data fusion of an arbitrary number of statistically independent features from one or arbitrarily many imaging modalities.
UR - http://www.scopus.com/inward/record.url?scp=84870589095&partnerID=8YFLogxK
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U2 - 10.1109/MFI.2012.6343021
DO - 10.1109/MFI.2012.6343021
M3 - Conference contribution
AN - SCOPUS:84870589095
SN - 9781467325110
T3 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
SP - 192
EP - 199
BT - 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2012 - Conference Proceedings
T2 - 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2012
Y2 - 13 September 2012 through 15 September 2012
ER -