Method and apparatus for detecting the presence of an occupant within a vehicle

• US 20060291697A1
• Filed: 06/21/2005
• Published: 12/28/2006
• Est. Priority Date: 06/21/2005
• 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
• Application Number:
  US11/158,093
• Publication Number:
  US 20060291697A1
• Time in Patent Office: Days
• US Class Current: 382/104
• CPC Class Codes: G06V 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition

Abstract

Systems and methods are provided for detecting an occupant within a vehicle. An image of a vehicle interior, containing depth information for a plurality of image pixels, is generated at an image sensor (110). The vehicle interior is divided into at least one blob of contiguous pixels (160). The at least one blob is divided into a series of layers (170) wherein each successive layer represents a range of depth within the image. It is determined if the at least one blob represents an occupant according to at least one characteristic of the series of layers (190).

Claims

1. A method for detecting an occupant within a vehicle comprising:

generating an image of a vehicle interior, containing depth information for a plurality of image pixels, at an image sensor;

dividing the image of the vehicle interior into at least one blob of contiguous pixels;

dividing the at least one blob into a series of layers, wherein each layer in the series of layers represents a range of depth within the image; and

determining if the at least one blob represents an occupant according to at least one characteristic of the series of layers.

2. A method as set forth in claim 1, wherein the step of determining if the at least one blob represents an occupant according to at least one characteristic of the series of layers comprises the steps of:

generating statistics for each layer according to at least one associated characteristic; and

determining if the at least one blob represents an occupant according the generated statistics.

3. A method as set forth in claim 2, wherein the step of generating statistics for each layer includes the step of calculating the percentage of total pixels within the at least one blob that have a depth value associated with the layer.

4. A method as set forth in claim 3, wherein the step of determining if the at least one blob represents an occupant includes the step of comparing the calculated percentage for a given layer to a threshold value associated with the layer.

5. A method as set forth in claim 2, wherein the step of determining if the at least one blob represents an occupant comprises the steps of:

providing the generated statistics for each layer to a pattern recognition classifier; and

determining an occupant class for the at least one blob at the pattern recognition classifier.

6. A method as set forth in claim 1, wherein the step of determining if the at least one blob represents an occupant according to at least one characteristic of the series of layers comprises the steps of:

identifying candidate objects within each of the series of layers; and

matching the candidate objects to at least one set of templates, a given template in the at least one set of templates being associated with one of a car seat and a portion of a human body.

7. A method as set forth in claim 6, wherein each of the series of layers has an associated set of templates from the at least one set of templates and the step of matching the candidate objects comprises the step of matching the identified candidate objects within a given layer to the set of templates associated with the layer.

8. A method as set forth in claim 1, wherein the step of determining if the at least one blob represents an occupant according to at least one characteristic of the layers comprises the steps of:

detecting motion within at least one layer of the series of layers over a period of time; and

determining if the at least one blob represents an occupant according the detected motion.

9. A method as set forth in claim 1, further comprising the step removing a vehicle seat from the image.

10. A method as set forth in claim 1, further comprising the step of alerting a driver of the vehicle, via an alarm, if the at least one blob represents an occupant.

11. The method of claim 10, further comprising the steps of:

classifying the at least one blob to determine an associated occupant class; and

varying the alarm according to the associated occupant class of the at least one blob.

12. The method of claim 1, wherein the step of determining if the at least one blob represents an occupant comprises the steps of:

classifying the at least one blob-to determine an associated occupant class; and

comparing the determined occupant class of the at least one blob to at least one occupant class determined for the at least one blob in a previous image of the vehicle interior.

13. The method of claim 1, wherein the image sensor is located in a headliner of the vehicle interior.

14. A system for determining if an occupant is present in a region of interest within a vehicle interior comprising:

an image generator that generates an image of the region of interest, containing depth information for a plurality of image pixels;

a blob segmentation component that isolates at least one blob of contiguous pixels within the image;

a layer segmentation component that divides the identified at least one blob into a plurality of layers, wherein a given pixel within the at least one blob is assigned to a corresponding layer according to its distance from the image generator; and

an occupant classifier that determines an occupant class for at least one blob according to at least one characteristic of the layers associated with the at least one blob.

15. The system of claim 14, the occupant classifier being operative to identify candidate objects associated with each layer of the at least one blob and match a given identified candidate object to a set of templates for the associated layer of the candidate object.

16. The system of claim 14, the occupant classifier comprising a pattern recognition classifier that classifies the at least one blob according to a plurality of features associated with the layers comprising the at least one blob.

17. The system of claim 16, the occupant classifier being operative to condense an image of the at least one blob into a downsized image comprising a plurality of pixels in which each pixel of the downsized image has a depth value representing an average depth value for a defined region of pixels within the at least one blob, the plurality of features comprising the depth values of the pixels comprising the downsized image.

18. The system of claim 14, each of the plurality of layers being parallel to a bottom of a seat within the vehicle interior.

19. The system of claim 14, the image generator being operative to generate depth information for the image via a time of flight system.

20. The system of claim 14, the image generator comprising a stereovision system operative to generate a stereo disparity map of the vehicle interior.

21. A computer program product, implemented in a computer readable medium and operative in a data processing system, for determining if an occupant is present in a region of interest from an image of the region of interest, containing depth information for a plurality of image pixels, comprising:

a blob segmentation component that isolates at least one blob of contiguous pixels within the image;

a layer segmentation component that divides the at least one blob into a plurality of layers, a given layer being associated with a range of depth within the image; and

an occupant classifier that determines an occupant class for the at least one blob according to at least one characteristic of the layers associated with the at least one blob.

22. The computer program product of claim 21, the occupant classifier comprising a rule based classifier, and the at least one characteristic comprising a percentage of the total pixels comprising the at least one blob that are associated with a given layer of the at least one blob.

23. The computer program product of claim 21, the occupant classifier comprising at least one of an artificial neural network and a support vector machine, and the at least one characteristic comprising first and second moments of the pixels comprising a given layer of the at least one blob.

24. The computer program product of claim 21, the at least one characteristic comprising detected motion within each layer of the at least one blob.