Abstract: A vehicle interior classification system and method in accordance with the present invention determines a classification relating to the interior of the vehicle, such as the occupancy status of a vehicle seat or the state of alertness of a vehicle driver, from one or more images of an appropriate portion of the interior of the vehicle acquired with an image capture device. The acquired images are preferably processed to limit the dynamic range of the images to obtain a resultant image which can comprise one or more regions of interest which are less than the total field of view of the image capture device. The resultant images are processed to extract information about features in the image and, in one embodiment, this processing is achieved with a two-dimensional complex discrete wavelet transform which produces a set of coefficients corresponding to the presence and/or location of the features in the resultant image.

Abstract: A system and method for processing acquired images to develop useful classifications of subjects such as occupants of a vehicle preferably employs a hierarchical and probabilistic structure, such as a Bayesian Network to analyze acquired images and produce a meaningful classification. The structure preferably includes set of analyzers, a set of Scenario analyzers and a set of Temporal models which are arranged in three respective hierarchical layers. Each respective analyzer operates on the acquired image and, in some circumstances, feedback from the Scenario analyzers, to produce an output representing the probability that a feature that the respective analyzer is concerned with is present in the acquired image.