초록 ▼

A object on a seat of a motor vehicle is classified by creating a video image of the area and forming a silhouette of the object. The silhouette is divided into two segments and a separate ellipse is positioned to approximate the shape of each segment. The parameters that define the location and siz

A object on a seat of a motor vehicle is classified by creating a video image of the area and forming a silhouette of the object. The silhouette is divided into two segments and a separate ellipse is positioned to approximate the shape of each segment. The parameters that define the location and size of the two ellipses form a feature vector for the object. A Bayesian classification function utilizes the feature vector to determine the probability that the object fits which each of a plurality of classes. A class for the object is determined based on the probabilities. This method can be used to control operation of an air bag in the motor vehicle in response to the class of the object on the seat.

대표청구항 ▼

1. A method for classifying a object that is present within a motor vehicle, that method comprising:acquiring an image of the object; segmenting the object into a first segment and a second segment; approximating the first segment with a first geometric shape which is selected from a group consistin

1. A method for classifying a object that is present within a motor vehicle, that method comprising:acquiring an image of the object; segmenting the object into a first segment and a second segment; approximating the first segment with a first geometric shape which is selected from a group consisting of a circle, an ellipse, and a regular polygon and which is defined by a first set of parameters; approximating the second segment with a second geometric shape which is selected from a group consisting of a circle, an ellipse, and a regular polygon and which is defined by a second set of parameters; forming a feature vector from the first and second sets of parameters; comparing the feature vector to plurality of template vectors, each representing different class of objects, to determine probabilities that the object falls within each class; and classifying the object in response to the probabilities.