Sensing cargo using an imaging device
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.
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.