A reliability guided sensor fusion model for optimal weighting in multimodal systems

For intelligent sensory systems, it is highly desirable to develop assessment methods that can continuously evaluate the reliability of potential sensory strategies taking into consideration changes in observation conditions. This relies on measuring a set of complementary features from multiple sen...

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Bibliographic Details
Published in2008 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 2453 - 2456
Main Authors Makkook, M., Basir, O., Karray, F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2008
Subjects
Bayesian methods
Dispersion
Genetic algorithms
Intelligent sensors
Intelligent systems
Multisensor systems
Noise measurement
Reliability estimation
Sensor fusion
Sensor phenomena and characterization
Speech recognition
Weight measurement
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Summary:For intelligent sensory systems, it is highly desirable to develop assessment methods that can continuously evaluate the reliability of potential sensory strategies taking into consideration changes in observation conditions. This relies on measuring a set of complementary features from multiple sensors and combining these features in an "intelligent" way that maximizes information gather and minimizes the impact of noise coming from the individual sensors. In this work, we formulate a statistical assessment method for estimating the reliability of observation conditions and propose an optimal mapping into weighting measures using genetic algorithms. Our approach is particularly beneficial for multimodal systems such as audio-visual speech recognition (AVSR).
ISBN:9781424414833
1424414830
ISSN:1520-6149
DOI:10.1109/ICASSP.2008.4518144