Locating a fire inside of a structure that is not in the direct field of view of the robot has been researched for intelligent firefighting\nrobots. By classifying fire, smoke, and their thermal reflections, firefighting robots can assess local conditions, decide a proper\nheading, and autonomously navigate toward a fire. Long-wavelength infrared camera images were used to capture the scene\ndue to the camera�s ability to image through zero visibility smoke. This paper analyzes motion and statistical texture features\nacquired from thermal images to discover the suitable features for accurate classification. Bayesian classifier is implemented to\nprobabilistically classify multiple classes, and a multiobjective genetic algorithm optimization is performed to investigate the\nappropriate combination of the features that have the lowest errors and the highest performance. The distributions of multiple\nfeature combinations that have 6.70% or less error were analyzed and the best solution for the classification of fire and smoke was\nidentified.
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