SYSTEM AND METHOD FOR DETECTING SEAT BELT VIOLATIONS FROM FRONT VIEW VEHICLE IMAGES

• US 20160078306A1
• Filed: 09/15/2014
• Published: 03/17/2016
• Est. Priority Date: 09/15/2014
• Status: Active Grant

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Loce, Robert P., Paul, Peter, Artan, Yusuf, BULAN, Orhan
• Granted Patent: US 9,552,524 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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: B60R 2022/4816 for sensing locking of buckle
• CPC Class Codes: B60R 2022/485 for sensing belt anchor position, belt orientation, or the like
• CPC Class Codes: B60R 22/48 Control systems, alarms, or interlock systems, for the correct application of the belt or harness
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06V 10/751 Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/59 inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• CPC Class Codes: G06V 20/593 Recognising seat occupancy

Abstract

A system and method for detecting a seat belt violation in a vehicle. The method may include receiving an image including a region of interest where a seat belt is visible if the seat belt is buckled. One or more first features may be extracted from the image. The one or more first features may be compared to one or more second features extracted from a plurality of sample images. It may be determined whether the seat belt is buckled in the image based upon the comparison.

Claims

1. A method for detecting a seat belt violation in a vehicle, comprising:

receiving an image including a region of interest where a seat belt is visible if the seat belt is buckled;

extracting one or more first features from the image;

comparing the one or more first features to one or more second features extracted from a plurality of sample images; and

determining whether the seat belt is buckled in the image based upon the comparison.

2. The method of claim 1, wherein the seat belt is buckled in at least one of the sample images, and the seat belt is unbuckled, and thus not visible, in at least one of the sample images.

3. The method of claim 1, wherein the image includes a front side of a vehicle.

4. The method of claim 3, further comprising locating a passenger in a front seat of the vehicle.

5. The method of claim 4, further comprising defining the region of interest relative to a location of the passenger.

6. The method of claim 3, further comprising detecting a windshield in the image.

7. The method of claim 6, wherein the one or more features are extracted from the region of interest in the image, and wherein the region of interest is smaller than the windshield.

8. The method of claim 6, further comprising assigning a plurality of landmark points relative to the windshield.

9. The method of claim 8, further comprising defining the region of interest relative to the landmark points.

10. The method of claim 1, wherein the one or more first features comprises a Fisher Vector, a bag-of-words, a histogram of oriented gradients, Harris Corners, local binary patterns, speeded-up robust features, scale-invariant feature transforms, or a combination thereof.

11. A method for detecting a seat belt violation in a vehicle, comprising:

receiving a plurality of first images, each including a region of interest where a seat belt is visible if the seat belt is buckled;

extracting one or more first features from each of the first images;

training a classifier to determine whether the seat belt is buckled using the one or more first extracted features;

receiving a second image including the region of interest;

extracting one or more second features from the second image;

comparing the one or more second extracted features to the one or more first extracted features; and

determining whether the seat belt is buckled in the second image based upon the comparison.

12. The method of claim 11, wherein the seat belt is buckled in at least one of the first images, and the seat belt is unbuckled, and thus not visible, in at least one of the first images.

13. The method of claim 12, further comprising locating a windshield in the image, wherein the one or more second features are extracted from the region of interest in the second image, and wherein the region of interest is smaller than the windshield.

14. The method of claim 13, further comprising defining the region of interest relative to a location of a passenger'"'"'s face visible through the windshield.

15. The method of claim 13, further comprising:

assigning a plurality of landmark points relative to the windshield; and

defining the region of interest relative to the landmark points.

16. The method of claim 13, further comprising creating one or more global feature representations using the one or more second features.

17. The method of claim 13, wherein the classifier comprises a support vector machine, a decision tree, a neural network, or a combination thereof.

18. A system for detecting a seat belt violation in a vehicle, comprising:

a training device configured to;

receive a plurality of first images, each including a region of interest where a seat belt is visible if the seat belt is buckled;

extract one or more first features from each of the first images; and

determine whether the seat belt is buckled using the one or more first extracted features; and

a determination device configured to;

receive a second image including the region of interest;

extract one or more second features from the second image;

compare the one or more second extracted features to the one or more first extracted features; and

determine whether the seat belt is buckled in the second image based upon the comparison.

19. The system of claim 18, further comprising:

an image capture device configured to capture first images, the second image, or both; and

an illuminator connected to the image capture device.

20. The system of claim 18, wherein the seat belt is buckled in at least one of the first images, and the seat belt is unbuckled, and thus not visible, in at least one of the first images.