Adaptive visual occupant detection and classification system

• US 20040186642A1
• Filed: 02/20/2004
• Published: 09/23/2004
• Est. Priority Date: 02/20/2003
• Status: Active Grant

• Current Assignee: Intelligent Mechatronic Systems Inc. (Trak Global Group Ltd.)
• Original Assignee: Intelligent Mechatronic Systems Inc. (Trak Global Group Ltd.)
• Inventors: Bullock, David, Basir, Otman Adam, Breza, Emil
• Granted Patent: US 8,560,179 B2
• Time in Patent Office: Days
• US Class Current: 701/45
• CPC Class Codes: B60N 2/002 Seats provided with an occupancy detection means mounted therein or thereon detection means responsive to presence or absence of children B60N2/266
• CPC Class Codes: B60N 2/286 forward facing B60N2/2866 takes precedence
• CPC Class Codes: B60N 2/2863 backward facing
• CPC Class Codes: B60R 21/01538 for image processing, e.g. cameras or sensor arrays
• CPC Class Codes: B60R 21/01542 detecting passenger motion
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

Abstract

A vision-based vehicle occupant detection and classification system includes a camera mounted in the vehicle interior to capture low resolution images of the seating area. Image processing algorithms account for lighting, motion, and other phenomena. A spatial-feature matrix numerically describes the content of the image. This descriptor is the result of a number of digital filters being run against a set of sub-images, derived from pre-defined window regions in the original image. This spatial-feature matrix is used as an input to an expert classifier function, which classifies this image as best representing a scenario in which the seat is (i) empty, (ii) occupied by an adult, (iii) occupied by a child, (iv) occupied by a rear-facing infant seat (RFIS), (v) occupied by a front-facing infant seat (FFIS), or (vi) occupied by an undetermined object.

Claims

1. A method for classifying an occupant including the steps of:

a) capturing an image of an occupant area;

b) dividing the image into a plurality of subimages of predetermined spatial regions;

c) generating a spatial feature matrix of the image based upon the plurality of subimages;

d) analyzing the spatial feature matrix; and

e) classifying an occupant in the occupant area based upon said step d).

2. The method of claim 1 further including the step of processing the image to account for lighting and motion before said step d).

3. The method of claim 1 further including the step of smoothing the classification of the occupant over time.

4. The method of claim 1 further including the step of determining whether to activate an active restraint based upon the classification of said step e).

5. The method of claim 1 wherein said step d) further includes the step of applying expert classifier algorithm to the spatial feature matrix.

6. The method of claim 5 wherein said step d) further includes the step of analyzing the spatial feature matrix based upon a set of training data.

7. The method of claim 6 further including the step of creating the set of training data by capturing a plurality of images of known occupant classifications of the occupant area.

8. The method of claim 5 wherein the expert classifier algorithm includes a neural network.

9. The method of claim 1 wherein said step d) is based upon system parameters including an orientation or a location from which the image is captured relative to the occupant area in said step a).

10. The method of claim 9 further including the step of:

f) altering the orientation or the location from which the image is captured and adjusting the system parameters.

11. The method of claim 10 wherein said step f) further includes the step of entering physical data representing a physical orientation and location of the occupant area.

12. The method of claim 10 wherein said step f) further includes the step of capturing a calibration image of the occupant area in a known condition and determining the system parameters based upon the calibration image.

13. The method of claim 12 wherein said step f) further includes the step of placing a calibration pattern on the occupant area before the step of capturing the calibration image, such that the calibration image includes the calibration pattern.

14. The method of claim 1 wherein the plurality of subimages overlap one another.

15. A vehicle occupant classification system comprising:

an image sensor for capturing an image of an occupant area; and

a processor dividing the image into a plurality of subimages, the processor analyzing the subimages to determine a classification of the occupant.

16. The vehicle occupant classification system of claim 15 wherein the processor determines the classification of the occupant from among the classifications including:

adult, child and infant seat.

17. The vehicle occupant classification system of claim 16 wherein the processor determines the classification of the occupant from among the classifications including:

adult, child, forward-facing infant seat and rearward-facing infant seat.

18. The vehicle occupant classification system of claim 15 wherein the processor generates a spatial feature matrix based upon the plurality of subimages.

19. The vehicle occupant classification system of claim 18 further including at least one filter generating the spatial feature matrix based upon. the plurality of subimages.

20. The vehicle occupant classification system of claim 19 further including an image processor for altering the image based upon lighting conditions and based upon motion.

21. The vehicle occupant classification system of claim 20 wherein the processor analyzes the spatial feature matrix to determine the occupant classification using a neural network.

22. The vehicle occupant classification system of claim 21 further including a temporal smoothing filter applying a decaying weighting function to a plurality of previous occupant classifications to determine a present occupant classification.

23. The vehicle occupant classification system of claim 22 further including a confidence weighting function applied to the plurality of previous occupant classifications to determine the present occupant classification.

24. The vehicle occupant classification system of claim 15 further including a plurality of digital filters extracting low-level descriptors from each of the subimages, the processor analyzing the low-level descriptors to determine the classification of the occupant.

25. A method for classifying an occupant including the steps of:

a) capturing an image of an occupant area;

b) dividing the image into a plurality of subimages of predetermined spatial regions;

c) generating a plurality of low-level descriptors from each of the plurality of subimages;

d) analyzing the low-level descriptors; and

e) classifying an occupant in the occupant area based upon step d).

26. The method of claim 25 wherein said step d) further includes the step of analyzing the low-level descriptors based upon a set of training data.

27. The method of claim 26 further including the step of creating the set of training data by capturing a plurality of images of known occupant classifications of the occupant area.

28. The method of claim 25 wherein said steps d) and e) are performed using a neural network.

29. The method of claim 25 wherein said step d) is based upon system parameters including an orientation or a location from which the image is captured relative to the occupant area.

30. The method of claim 29 further including the step of:

f) altering the orientation or the location from which the image is captured and adjusting the system parameters.

31. The method of claim 30 wherein said step f) further includes the step of entering physical data representing a physical orientation and location of the occupant area.