Sensing cargo using an imaging device

• US 7,746,379 B2
• Filed: 12/31/2002
• Issued: 06/29/2010
• Est. Priority Date: 12/31/2002
• Status: Active Grant

• Current Assignee: Asset Intelligence LLC (PowerFleet, Inc.)
• Original Assignee: Asset Intelligence LLC (PowerFleet, Inc.)
• Inventors: Calver, Andrew J., Jesson, Joseph E., Cobello, Rick
• Primary Examiner(s): Chan; Jason
• Assistant Examiner(s): Giles; Nicholas G
• Application Number:
  US10/335,372
• Publication Number:
  US 20040125217A1
• Time in Patent Office: 2,737 Days
• Field of Search: 348/86, 348/91, 348/94, 348/231.3, 348/161, 382/141, 382/143, 382/181, 382/224, 382/155, 382/156, 382/157, 382/158, 382/159, 340/568.1, 702/128
• US Class Current: 348/161
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

A camera is used to sense cargo in a cargo space. An image of at least a portion of a cargo space is captured using the camera, and a digital signal processor classifies the image as representing an empty cargo space or a non-empty cargo space. Additionally, a type of cargo represented by the captured image may also be classified by the digital signal processor and an indication of the type of cargo output.

Claims

1. A method comprising:

receiving a signal requesting an indication of whether cargo is present in a cargo space from a remote source;

capturing, using a light sensor, an image of at least a portion of the cargo space;

classifying, using a learning classifier located within the cargo space, the captured image to determine a type of content within the cargo space, wherein the type of content comprises making a determination of cargo capacity used and the manner of use; and

outputting, to a location that is remote from the cargo space and based on the classifying, an indication of a presence and characteristics of cargo, an absence of cargo or a presence and characteristics of non-cargo items.

2. A method as recited in claim 1, wherein the cargo space comprises an interior of a trailer.

3. A method as recited in claim 1, wherein capturing the image comprises capturing the image using an infrared (IR) imaging device.

4. A method as recited in claim 1, wherein capturing the image comprises capturing the image using an ultraviolet (UV) imaging device.

5. A method as recited in claim 1, wherein the learning classifier comprises a back-propagation of errors artificial neural network.

6. A method as recited in claim 1, wherein the learning classifier comprises a self-organizing Kohonen neural network.

7. A method as recited in claim 1, wherein the learning classifier comprises a relaxed-energy self-organizing artificial neural network.

8. A method as recited in claim 1, wherein the captured image is modified prior to the classifying.

9. A method as recited in claim 1, further comprising:

combining a plurality of captured images by one or more cameras of different or overlapping portions of the cargo space into a single image; and

wherein the classifying comprises using the single image as the captured image.

10. A method as recited in claim 9, wherein a first image of the plurality of captured images is captured in a first light frequency range, and wherein a second image of the plurality of captured images is captured in a second light frequency range.

11. A method as recited in claim 10, wherein the first image is captured by the light sensor of a first imaging device, and wherein the second image is captured by a light sensor of a second imaging device.

12. A system comprising:

a module to receive a signal, from a remote source, requesting an indication of whether cargo is present in a cargo space;

a camera to capture an image of at least a portion of the cargo space; and

a digital signal processor, coupled to receive the image from the camera, to classify the image as representing an empty cargo space or a non-empty cargo space, including;

a classification module located within the cargo space to extract characteristics from the image and determine, based on learned characteristics from training images, a type of content within the cargo space to provide an indication of an absence of cargo, a presence and characteristics of cargo or a presence and characteristics of non-cargo items, wherein classifying the type of content comprises making a determination of cargo capacity used and manner of use.

13. A system as recited in claim 12, wherein the digital signal processor is implemented in a single integrated circuit.

14. A system as recited in claim 12, wherein the camera comprises an infrared (IR) imaging device.

15. A system as recited in claim 12, wherein the camera comprises an ultraviolet (UV) imaging device.

16. A system as recited in claim 12, wherein the digital signal processor implements a back-propagation of errors artificial neural network.

17. A system as recited in claim 12, wherein the digital signal processor implements a self-organizing Kohonen neural network.

18. A system as recited in claim 12, wherein the digital signal processor implements a relaxed-energy self-organizing artificial neural network.

19. One or more computer readable media having computer readable instructions stored thereon that, when executed by a processor, causes the processor to:

receive a signal, from a remote source, requesting an indication of whether cargo is present in a cargo space;

access at least a portion of an image of at least a portion of the cargo space, wherein the image was generated by sensing light at a particular frequency range;

classify, from within the cargo space, the captured image to determine a type of content within the cargo space, wherein classifying the type of content comprises making a determination of cargo capacity used and the manner of use; and

output to a location remote from the cargo space, based on the classification, an indication of a presence and characteristics of cargo, an absence of cargo or a presence and characteristics of non-cargo items.

20. One or more computer readable media as recited in claim 19, wherein the particular frequency range comprises the infrared (IR) range.

21. One or more computer readable media as recited in claim 19, wherein the particular frequency range comprises the ultraviolet (UV) range.

22. A method comprising:

capturing, using a light sensor, an image of at least a portion of a cargo space;

classifying from within the cargo space, based on one or more training images, a type of cargo represented by the captured image to determine a type of content within the cargo space, wherein the type of cargo comprises making a determination of cargo capacity used and the manner of use; and

outputting, to a location remote from the cargo space and based on the classifying, an indication of a presence and characteristics of cargo, an absence of cargo or a presence and characteristics of non-cargo items.

23. A method as recited in claim 22, wherein the light sensor comprises an infrared (IR) imaging device.

24. A method as recited in claim 22, wherein the light sensor comprises an ultraviolet (UV) imaging device.

25. A method as recited in claim 22, wherein the classifying comprises classifying the type of cargo represented by the captured image using a back-propagation of errors artificial neural network.

26. A method as recited in claim 22, wherein the classifying comprises classifying the type of cargo represented by the captured image using a self-organizing Kohonen neural network.

27. A method as recited in claim 22, wherein the classifying comprises classifying the type of cargo represented by the captured image using a relaxed-energy self-organizing artificial neural network.

28. A system comprising:

a module to receive a signal, from a remote source, requesting an indication of whether cargo is present in a cargo space;

a camera to capture an infrared (IR) or ultraviolet (UV) image of at least a portion of the cargo space; and

a digital signal processor located within the cargo space implementing a learning classifier to classify the image to determine a presence and characteristics of cargo in the cargo space, an absence of cargo in the cargo space or a presence and characteristics of non-cargo items in the cargo space by classifying a type of content within the cargo space,

wherein classifying the type of content comprises making a determination of cargo capacity used and the manner of use, and

wherein the digital signal processor is coupled to receive the image from the camera.

29. A system as recited in claim 28, wherein the learning classifier comprises a back-propagation of errors artificial neural network.

30. A system as recited in claim 28, wherein the learning classifier comprises a self-organizing Kohonen neural network.