BI-OPTIC OBJECT CLASSIFICATION SYSTEM

Abstract

The present disclosure provides a system and method for identifying items. The method includes scanning a first item using a first barcode scanner and a second barcode scanner and determining, based on signals from the first barcode scanner and the second barcode scanner, a first two-dimensional light grid indicating where the first item broke beams of the first barcode scanner and the second barcode scanner. The method also includes predicting, using a machine learning model and based on the first two-dimensional light grid, a first category for the first item, receiving, from a camera, an image of the first item, and comparing the image of the first item to a first set of images assigned to the first category to determine an identity of the first item.

Description

Claims

1. A method comprising: scanning a first item using a first barcode scanner and a second barcode scanner;determining, based on signals from the first barcode scanner and the second barcode scanner, a first two-dimensional light grid indicating where the first item broke beams of the first barcode scanner and the second barcode scanner;predicting, using a machine learning model and based on the first two-dimensional light grid, a first category for the first item;receiving, from a camera, an image of the first item; andcomparing the image of the first item to a first set of images assigned to the first category to determine an identity of the first item.

2. The method of claim 1, wherein the first barcode scanner and the second barcode scanner are arranged such that the beams of the first barcode scanner and the second barcode scanner are in different planes.

3. The method of claim 1, wherein the first two-dimensional light grid indicates to the machine learning model a size and shape of the first item.

4. The method of claim 1, wherein the first category of the first item is one of produce or a boxed item.

5. The method of claim 1, further comprising: scanning a second item using the first barcode scanner and the second barcode scanner;determining, based on signals from the first barcode scanner and the second barcode scanner, a second two-dimensional light grid indicating where the second item broke the beams of the first barcode scanner and the second barcode scanner, andpredicting, using the machine learning model and based on the second two-dimensional light grid, that the second item is a hand.

6. The method of claim 5, further comprising: determining, based on a motion of the hand, that a third item is being taken by the hand; andgenerating a message indicating that the third item is being taken.

7. The method of claim 1, further comprising refraining from comparing the image of the first item to a second set of images assigned to a second category different from the first category.

8. A system comprising: a first barcode scanner arranged to scan a first item;a second barcode scanner arranged to scan the first item;a camera arranged to capture an image of the first item;a memory; anda hardware processor communicatively coupled to the memory, the hardware processor configured to: determine, based on signals from the first barcode scanner and the second barcode scanner, a first two-dimensional light grid indicating where the first item broke beams of the first barcode scanner and the second barcode scanner;predict, using a machine learning model and based on the first two-dimensional light grid, a first category for the first item; andcomparing the image of the first item to a first set of images assigned to the first category to determine an identity of the first item.

9. The system of claim 8, wherein the beams of the first barcode scanner and the second barcode scanner are in different planes.

10. The system of claim 8, wherein the first two-dimensional light grid indicates to the machine learning model a size and shape of the first item.

11. The system of claim 8, wherein the first category of the first item is one of produce or a boxed item.

12. The system of claim 8, wherein: the first barcode scanner and the second barcode scanner are further arranged to scan a second item;the hardware processor is further configured to: determine, based on signals from the first barcode scanner and the second barcode scanner, a second two-dimensional light grid indicating where the second item broke the beams of the first barcode scanner and the second barcode scanner; andpredict, using the machine learning model and based on the second two-dimensional light grid, that the second item is a hand.

13. The system of claim 12, wherein the hardware processor is further configured to: determine, based on a motion of the hand, that a third item is being taken by the hand; andgenerate a message indicating that the third item is being taken.

14. The system of claim 8, wherein the hardware processor is further configured to refrain from comparing the image of the first item to a second set of images assigned to a second category different from the first category.

15. A non-transitory computer readable medium storing instructions that, when executed by a hardware processor, cause the hardware processor to: determine, based on signals from a first barcode scanner and a second barcode scanner, a first two-dimensional light grid indicating where a first item broke beams of the first barcode scanner and the second barcode scanner when the first barcode scanner and the second barcode scanner scanned the first item;predict, using a machine learning model and based on the first two-dimensional light grid, a first category for the first item;receive, from a camera, an image of the first item; andcompare the image of the first item to a first set of images assigned to the first category to determine an identity of the first item.

16. The medium of claim 15, wherein the beams of the first barcode scanner and the second barcode scanner are in different planes.

17. The medium of claim 15, wherein the first two-dimensional light grid indicates to the machine learning model a size and shape of the first item.

18. The medium of claim 15, wherein the first category of the first item is one of produce or a boxed item.

19. The medium of claim 15, wherein the instructions, when executed, further cause the hardware processor to: determine, based on signals from the first barcode scanner and the second barcode scanner, a second two-dimensional light grid indicating where a second item broke the beams of the first barcode scanner and the second barcode scanner when the first barcode scanner and the second barcode scanner scanned the second item; andpredict, using the machine learning model and based on the second two-dimensional light grid, that the second item is a hand.

20. The medium of claim 19, wherein the instructions, when executed, further cause the hardware processor to: determine, based on a motion of the hand, that a third item is being taken by the hand; andgenerate a message indicating that the third item is being taken.