VEHICLE OCCUPANCY DETECTION USING PASSENGER TO DRIVER FEATURE DISTANCE

• US 20150324653A1
• Filed: 05/09/2014
• Published: 11/12/2015
• Est. Priority Date: 05/09/2014
• Status: Active Grant

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Xu, Beilei, Paul, Peter, Perronnin, Florent
• Granted Patent: US 9,760,783 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06V 20/593 Recognising seat occupancy
• CPC Class Codes: G08G 1/04 using optical or ultrasonic detectors

Abstract

A method, system, and apparatus for vehicle occupancy detection comprises collecting image data of a vehicle, creating a first value according to a plurality of characteristics of a driver position of the vehicle, and then creating at least one other value according to a plurality of characteristics of at least one other candidate occupant position in the vehicle. The characteristics of the driver position of the vehicle and candidate occupant position of the vehicle are compared in order to determine the vehicle occupancy.

Claims

1. A method for vehicle occupancy detection comprising:

collecting image data of a vehicle;

creating a first value according to a plurality of characteristics of a driver position of said vehicle;

creating at least one other value according to a plurality of characteristics of at least one other candidate occupant position in said vehicle; and

comparing said first value to said at least one other value in order to determine a vehicle occupancy.

2. The method of claim 1 wherein collecting image data of a vehicle further comprises:

localizing said image data of said vehicle in order to define a localized windshield region; and

splitting said localized windshield region into a driver part and a passenger part.

3. The method of claim 2 further comprising compensating for disparities in said localized windshield region by right/left flipping at least one of said driver part and said passenger part.

4. The method of claim 2 wherein creating said first value according to a plurality of characteristics of a driver position of said vehicle further comprises calculating a first image feature representation vector;

and

wherein creating at least one other value according to a plurality of characteristics of at least one other candidate occupant position in said vehicle further comprises calculating at least one other image feature representation vector.

5. The method of claim 4 wherein calculating a first image feature representation vector and calculating at least one other image feature representation vector further comprises:

extracting a set of patches;

computing a set of statistics from each of said patches; and

aggregating said set of statistics from each of said patches into at least one of an image-level representation and a region level representation.

6. The method of claim 4 wherein comparing said first image feature representation vector to said at least one other image feature representation vector further comprises calculating a distance between said first image feature representation vector and said at least one other image feature representation vector.

7. The method of claim 6 wherein said distance is calculated using at least one of:

a Euclidean distance; and

a Mahalanobis distance.

8. The method of claim 6 further comprising:

determining a threshold distance based on a training set of images wherein said threshold distance is indicative of a passenger in a vehicle; and

applying a classifier to said distance between said first image feature representation vector and said at least one other image feature representation vector in order to determine if said distance between said first image feature representation vector and said at least one other image feature representation vector is indicative of a passenger in said vehicle.

9. The method of claim 8 wherein said classifier comprises one of:

a kernel machine; and

a probabilistic clustering algorithm.

10. A method for vehicle occupancy detection comprising:

collecting image data of a vehicle;

localizing said image data of said vehicle in order to define a localized windshield region;

splitting said localized windshield region into a driver part and a passenger part;

creating a first value according to a plurality of characteristics of said driver part of said vehicle by calculating a first image feature representation vector;

creating at least one other value according to a plurality of characteristics of said passenger part of said vehicle by calculating at least one other image feature representation vector; and

calculating a distance between said first image feature representation vector and said at least one other image feature representation vector in order to determine a vehicle occupancy.

11. The method of claim 10 further comprising:

determining a threshold distance based on a training set of images wherein said threshold distance is indicative of a passenger in a vehicle; and

applying a classifier to said distance between said first image feature representation vector and said at least one other image feature representation vector in order to determine if said distance between said first image feature representation vector and said at least one other image feature representation vector is indicative of a passenger in said vehicle.

12. A system for vehicle occupancy detection comprising:

a camera module configured to collect image data of a vehicle;

a processor; and

a computer-usable medium embodying computer code, said computer-usable medium being coupled to said processor, said computer code comprising non-transitory instruction media executable by said processor configured for;

collecting image data of a vehicle;

creating a first value according to a plurality of characteristics of a driver position of said vehicle;

creating at least one other value according to a plurality of characteristics of at least one other candidate occupant position in said vehicle; and

comparing said first value to said at least one other value in order to determine a vehicle occupancy.

13. The system of claim 12 wherein collecting image data of a vehicle further comprises:

localizing said image data of said vehicle in order to define a localized windshield region; and

splitting said localized windshield region into a driver part and a passenger part.

14. The system of claim 13 wherein said computer code comprising non-transitory instruction media executable by said processor is further configured for:

compensating for disparities in said localized windshield region by right/left flipping at least one of said driver part and said passenger part.

15. The system of claim 13 wherein creating said first value according to a plurality of characteristics of a driver position of said vehicle further comprises calculating a first image feature representation vector;

and

wherein creating at least one other value according to a plurality of characteristics of at least one other candidate occupant position in said vehicle further comprises calculating at least one other image feature representation vector.

16. The system of claim 15 wherein calculating a first image feature representation vector and calculating at least one other image feature representation vector further comprises:

extracting a set of patches;

computing a set of statistics from each of said patches; and

aggregating said set of statistics from each of said patches into at least one of an image-level representation and a region level representation.

17. The system of claim 16 wherein comparing said first image feature representation vector to said at least one other image feature representation vector further comprises calculating a distance between said first image feature representation vector and said at least one other image feature representation vector.

18. The system of claim 17 wherein said distance is calculated using at least one of:

a Euclidean distance; and

a Mahalanobis distance.

19. The system of claim 17 wherein said computer code comprising non-transitory instruction media executable by said processor is further configured for:

determining a threshold distance based on a training set of images wherein said threshold distance is indicative of a passenger in a vehicle; and

applying a classifier to said distance between said first image feature representation vector and said at least one other image feature representation vector in order to determine if said distance between said first image feature representation vector and said at least one other image feature representation vector is indicative of a passenger in said vehicle.

20. The system of claim 19 wherein said classifier comprises one of:

a kernel machine; and

a probabilistic clustering algorithm.