Method and apparatus for classifying a vehicle occupant according to stationary edges

• US 20080231027A1
• Filed: 03/21/2007
• Published: 09/25/2008
• Est. Priority Date: 03/21/2007
• Status: Abandoned Application

• Current Assignee: TRW Automotive USLLC (Zeppelin - Stiftung der Stadt Friedrichshafen)
• Original Assignee: TRW Automotive USLLC (Zeppelin - Stiftung der Stadt Friedrichshafen)
• Inventors: Luo, Yun, David, Raymond J.
• Application Number:
  US11/726,460
• Publication Number:
  US 20080231027A1
• Time in Patent Office: Days
• US Class Current: 280/735
• CPC Class Codes: G06V 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition

Abstract

System and methods are provided for classifying an occupant of a vehicle. An edge image generation component (102) produces an edge image of a vehicle occupant. A long term filtering component (104) filters across a plurality of edge images to produce a static edge image. A feature extraction component (106) extracts a plurality of features from the static edge image. A classification component (108) selects an occupant class for the vehicle occupant according to the extracted plurality of features.

Claims

1. A method for classifying a vehicle occupant into one of a plurality of occupant classes, comprising:

producing a series of edge images of the vehicle occupant;

filtering across the series of edge images to produce a static edge image;

extracting a plurality of features from the static edge image; and

selecting an occupant class for the vehicle occupant according to the extracted plurality of features.

2. The method of claim 1, wherein filtering across a series of edge images of the vehicle occupant comprises:

blurring each of the series of edge images with a Gaussian filter;

averaging associated values of corresponding edge pixels across the series of edge images to produce an averaged edge image, with each pixel in the averaged edge image having an associated value equal to the averaged value of its corresponding edge pixels in the series of edge images; and

comparing the value of each pixel within the averaged edge image to a threshold value with pixels exceeding the threshold having a first value in the static edge image and pixels failing to exceed the threshold having a second value in the static edge image.

3. The method of claim 1, further comprising applying a filling routine to the static edge image to fill in gaps between proximate edge segments.

4. The method of claim 1, wherein extracting a plurality of features from the static edge image comprises calculating at least one set of descriptive statistics representing the individual edge segments comprising the static edge image.

5. The method of claim 1, wherein extracting a plurality of features from the static edge image comprises dividing the static edge image into a plurality of regions and determining at least one metric representing each region.

6. The method of claim 1, wherein extracting a plurality of features from the static edge image comprises defining a contour around the static edge image and extracting at least one feature from the defined contour.

7. The method of claim 6, wherein defining a contour around the static edge image comprises applying a convex hull algorithm to define a convex envelope around the static edge image.

8. The method of claim 1, wherein extracting a plurality of features from the static edge image comprises searching the static edge image for at least one of a plurality of stored templates, a given template being associated with at least one of the plurality of occupant classes.

9. The method of claim 8, wherein searching the static edge image for the at least one template includes searching a portion of the static edge image for a portion of the image that substantially matches a given template within a defined range of at least one of position, rotation, and scale.

10. A classification system for a vehicle occupant protection device, comprising:

an edge image generation component that produces an edge image of a vehicle occupant;

a buffer that stores a plurality of edge images produced by the edge image generation component;

a long term filtering component that filters across the plurality of edge images stored in the buffer to produce a static edge image;

a feature extraction component that extracts a plurality of features from the static edge image; and

a classification component that selects an occupant class for the vehicle occupant according to the extracted plurality of features.

11. The system of claim 10, the feature extraction component comprising a segment feature extractor that calculates at least one set of descriptive statistics representing individual edge segments comprising the static edge image.

12. The system of claim 10, the feature extraction component comprising a template matching element that searches the static edge image for at least one of a plurality of stored templates that are associated with respective occupant classes.

13. The system of claim 10, the classification component comprising an artificial neural network.

14. The system of claim 10, the long term filtering component comprising:

an averaging element that averages associated values of corresponding edge pixels across the plurality of edge images stored in the buffer to produce an averaged edge image; and

a thresholding element that compares the value of each pixel within the averaged edge image to a threshold value with pixels exceeding the threshold having a first value in the static edge image and pixels failing to meet the threshold having a second value in the static edge image.

15. The system of claim 10, further comprising an edge filling routine that fills in gaps between proximate edge segments in the static edge image.

16. A computer readable medium comprising a plurality of executable instructions that can be executed by a data processing system, the executable instructions comprising:

an edge image generation component that produces a series of edge images of a vehicle occupant;

a long term filtering component that filters across the series of edge images to produce a static edge image;

a feature extraction component that extracts a plurality of features from the static edge image;

a classification component that selects an occupant class for the vehicle occupant according to the extracted plurality of features; and

a controller interface that provides the selected occupant class to a vehicle occupant protection device.

17. The computer readable medium of claim 16, the feature extraction component further comprising an appearance based feature extractor that divides the static edge image into a plurality of regions and determines at least one metric representing each region.

18. The computer readable medium of claim 16, the feature extraction component further comprising a contour feature extractor that defines a contour around the static edge image and extracts at least one feature from the defined contour.

19. The computer readable medium of claim 16, the classification component comprising a rule based classifier that applies at least one logical rule to the extracted features to select an occupant class.

20. The computer readable medium of claim 16, the classification component comprising a support vector machine.