Vision-based occupant classification method and system for controlling airbag deployment in a vehicle restraint system
First Claim
1. A method of vehicle occupant classification for controlling airbag deployment in a vehicle restraint system comprising the steps of:
- receiving a first image from a first sensor of a predetermined region of interest occupied by a vehicle occupant by an occupant-classification controller;
simultaneously receiving a second image from a second sensor of the predetermined region of interest occupied by the vehicle passenger by the occupant-classification controller;
dividing the first and second images into an array of equal sized disparity areas;
computing a first texture matrix and a second texture matrix for the respective first and second images;
calculating a single disparity-map-estimate from the first texture matrix and the second texture matrix;
performing an iterative neighborhood update to achieve a disparity map;
applying a pre-determined average disparity threshold map to the disparity map to achieve a binary map;
extracting a refined disparity map from the binary map;
inputting the refined disparity map into a neural network classifier of the occupant-classification controller; and
outputting a control signal for controlling airbag deployment in the vehicle restraint system.
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Accused Products
Abstract
A vehicle restraint system has a vision-based occupant classification system for control of airbag deployment during a crash scenario. The classification system utilizes two imaging sensors which together create a stream of paired images received and stored by an occupant classification controller. A computer program product of the controller utilizes the paired images to extract disparity/range features and stereo-vision differential edge density features. Moreover, the controller extracts wavelet features from one of the two paired images. All three features or maps are classified amongst preferably seven classifications by algorithms of the computer program product producing class confidence data fed to a sensor fusion engine of the controller for processing and output of an airbag control signal input into a restraint controller of the vehicle restraint system.
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Citations
17 Claims
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1. A method of vehicle occupant classification for controlling airbag deployment in a vehicle restraint system comprising the steps of:
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receiving a first image from a first sensor of a predetermined region of interest occupied by a vehicle occupant by an occupant-classification controller;
simultaneously receiving a second image from a second sensor of the predetermined region of interest occupied by the vehicle passenger by the occupant-classification controller;
dividing the first and second images into an array of equal sized disparity areas;
computing a first texture matrix and a second texture matrix for the respective first and second images;
calculating a single disparity-map-estimate from the first texture matrix and the second texture matrix;
performing an iterative neighborhood update to achieve a disparity map;
applying a pre-determined average disparity threshold map to the disparity map to achieve a binary map;
extracting a refined disparity map from the binary map;
inputting the refined disparity map into a neural network classifier of the occupant-classification controller; and
outputting a control signal for controlling airbag deployment in the vehicle restraint system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A vision-based occupant classification system for controlling airbag deployment in a vehicle restraint system having a computer-based processor, at least one accelerometer for detection of a crash scenario by inputting a signal to the processor, and an airbag for controlled deployment during the crash scenario and being controlled by a restraint controller, the vision-based occupant classification system comprising:
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a first and a second imaging sensor for simultaneous imaging of a predetermined region of interest;
a first image and a second image produced by the respective first and second imaging sensors;
a computer-based occupant-classification controller for classification of the pair of images, the occupant-classification controller having a memory;
a computer program software medium encoded into the occupant-classification controller;
wavelet features extracted from the first image;
disparity features extracted from the first and second images;
differential edge density features extracted from the first and second images; and
a neural network classifier of the computer program software medium for computation of the wavelet, disparity and differential edge density features processed by the computer program software medium for processing of an array of class confidence data into an airbag control signal received by the vehicle restraint controller. - View Dependent Claims (15, 16, 17)
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Specification