Apparatuses, systems and methods for classifying digital images
The present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle by comparing current image data to previously classified image data.
1. A vehicle in-cabin imaging device, the vehicle in-cabin imaging device comprising:
a processor and a memory, wherein previously classified image data is stored on the memory wherein the previously classified image data is representative of known images of at least one vehicle interior, wherein the previously classified image data is normalized for a range of different drivers, and wherein the previously classified image data is representative of at least one of: a vehicle occupant elbow orientation, or a seat belt location;
at least one sensor for generating current image data, wherein the current image data is representative of current images of a vehicle interior, and wherein the current image data is representative of a current vehicle occupant elbow orientation; and
a current image classification module stored on the memory that, when executed by the processor, causes the processor to classify current images of the vehicle interior based on a comparison of the current image data with the previously classified image data, wherein at least one current image is classified as representative of the current vehicle occupant elbow orientation.
2. The vehicle in-cabin imaging device as in claim 1 , wherein the at least one sensor is selected from: at least one digital image sensor, at least one ultra-sonic sensor, at least one radar-sensor, at least one infrared light sensor, or at least one laser light sensor.
3. The vehicle in-cabin imaging device as in claim 1 , wherein the current image data is representative of a three-dimensional location of at least one vehicle occupant within the vehicle interior.
4. The vehicle in-cabin imaging device as in claim 1 , wherein the current image data is representative of an orientation of at least a portion of at least one vehicle occupant within the vehicle interior.
5. The vehicle in-cabin imaging device as in claim 1 , wherein the current image data is representative of a three-dimensional location of at least a portion of at least one vehicle occupant within the vehicle interior and an orientation of the portion of the at least one vehicle occupant.
6. The vehicle in-cabin imaging device as in claim 1 , wherein the current image data is representative of at least one of: a vehicle occupant head location/orientation, a vehicle occupant hand location/orientation, a vehicle occupant arm location/orientation, a vehicle occupant torso location/orientation, a cellular telephone location, a vehicle occupant eye location/orientation, or a vehicle seat location/orientation.
7. The vehicle in-cabin imaging device as in claim 1 , wherein the previously classified image data is representative of both the vehicle occupant elbow orientation, and the seat belt location.
8. A computer-implemented method for automatically classifying images of an interior of a vehicle, the method comprising:
receiving previously classified image data at a processor, from a remote computing device, in response to the processor executing a previously classified image data receiving module, wherein the previously classified image data is representative of known images of at least one vehicle interior, wherein the previously classified image data is normalized for a range of different drivers, and wherein the previously classified image data is representative of at least one of: a vehicle occupant elbow orientation, or a seat belt location;
receiving current image data at the processor, from at least one sensor, wherein the current image data is representative of current images of a vehicle interior, and wherein the current image data is representative of a current vehicle occupant elbow orientation; and
classifying current images, using the processor, based on a comparison of the current image data with the previously classified image data, wherein at least one current image is classified as representative of the current vehicle occupant elbow orientation.
9. The computer-implemented method of claim 8 , wherein the current image data is representative of at least one of: a vehicle occupant head location/orientation, a vehicle occupant hand location/orientation, a vehicle occupant arm location/orientation, a vehicle occupant torso location/orientation, a cellular telephone location, a vehicle occupant eye location/orientation, or a vehicle seat location/orientation.
10. The computer-implemented method of claim 8 , wherein the previously classified image data is representative of both the vehicle occupant elbow orientation, and the seat belt location.
11. The computer-implemented method of claim 8 , wherein the current image data is representative of a three-dimensional location of at least one vehicle occupant within the vehicle interior.
12. The computer-implemented method of claim 8 , wherein the current image data is representative of an orientation of at least a portion of at least one vehicle occupant within the vehicle interior.
13. The computer-implemented method of claim 8 , wherein the current image data is representative of a three-dimensional location of at least a portion of at least one vehicle occupant within the vehicle interior and an orientation of the portion of the at least one vehicle occupant.
14. A non-transitory computer-readable medium storing computer-readable instructions that, when executed by a processor, cause the processor to automatically classify images of an interior of a vehicle, the non-transitory computer-readable medium comprising:
a previously classified image data receiving module that, when executed by the processor, causes the processor to receive previously classified image data from a remote computing device, wherein the previously classified image data is representative of known images of at least one vehicle interior, wherein the previously classified image data is normalized for a range of different drivers, and wherein the previously classified image data is representative of at least one of: a vehicle occupant elbow orientation, or a seat belt location;
a current image data receiving module that, when executed by the processor, causes the processor to receive current image data from at least one sensor, wherein the current image data is representative of current images of a vehicle interior, and wherein the current image data is representative of a current vehicle occupant elbow orientation; and
a current image classification module that, when executed by the processor, causes the processor to classify current images based on a comparison of the current image data with the previously classified image data, wherein at least one current image is classified as representative of the current vehicle occupant elbow orientation.
15. The non-transitory computer-readable medium of claim 14 , wherein the current image data is representative of a three-dimensional location of at least a portion of at least one vehicle occupant within the vehicle interior and an orientation of the portion of the at least one vehicle occupant.
16. The non-transitory computer-readable medium of claim 14 , wherein the current image data is representative of an orientation of at least a portion of at least one vehicle occupant within the vehicle interior.
17. The non-transitory computer-readable medium of claim 14 , wherein the current image data is representative of at least one of: a vehicle occupant head location/orientation, a vehicle occupant hand location/orientation, a vehicle occupant arm location/orientation, a vehicle occupant torso location/orientation, a cellular telephone location, a vehicle occupant eye location/orientation, or a vehicle seat location/orientation.
18. The non-transitory computer-readable medium of claim 14 , wherein the previously classified image data is representative of both the vehicle occupant elbow orientation, and the seat belt location.
19. The non-transitory computer-readable medium of claim 14 , wherein the at least one sensor is selected from: at least one ultra-sonic sensor, at least one radar-sensor, at least one infrared light sensor, or at least one laser light sensor.
20. The vehicle in-cabin imaging device as in claim 1 , wherein the current image data is representative of a current seat belt location.