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DE-102021112659-B4 - Using barcodes to determine the dimensions of an object

DE102021112659B4DE 102021112659 B4DE102021112659 B4DE 102021112659B4DE-102021112659-B4

Abstract

Method for detecting inadmissible objects using a barcode reader (106), wherein the method comprises: Identifying a barcode (124) in an image of an object (152); Determining a reference value assigned to the barcode (124); Identifying a visual feature of the object (152) that is located outside the barcode (124); Comparing the reference value assigned to the barcode (124) with the visual feature and, in response, determining at least one dimensional property of the object (152); and as a response to determining the at least one dimensional property, comparing the at least one dimensional property with at least one reference dimensional property of the object (152), and as a response to a mismatch between the at least one dimensional property and the at least one reference dimensional property, determining that a detection event of an invalid object (152) has occurred; wherein the visual feature comprises one or more physical features and/or printed features; including determining the reference quantity assigned to the barcode (124): Decoding a barcode payload (124); and Determining one dimension of the barcode (124) from the payload.

Inventors

  • Carles Burton Swope
  • Barkan Edward
  • Drzymala Mark
  • Darran Michael Handshaw

Assignees

  • ZEBRA TECHNOLOGIES CORPORATION

Dates

Publication Date
20260513
Application Date
20210517
Priority Date
20200529

Claims (20)

  1. Method for detecting impermissible objects using a barcode reader (106), comprising: identifying a barcode (124) in an image of an object (152); determining a reference size associated with the barcode (124); identifying a visual feature of the object (152) located outside the barcode (124); comparing the reference size associated with the barcode (124) with the visual feature and, in response thereto, determining at least one dimensional property of the object (152); and, in response to determining the at least one dimensional property, comparing the at least one dimensional property with at least one reference dimensional property of the object (152), and in response to a mismatch between the at least one Dimensional property and the at least one reference dimension property, determining that a detection event of an invalid object (152) has occurred; wherein the visual feature comprises one or more of a physical feature and/or a printed feature; wherein determining the reference associated with the barcode (124) comprises: decoding a payload of the barcode (124); and determining a dimension of the barcode (124) from the payload.
  2. Procedure according to Claim 1 , where determining the reference includes: determining one dimension of the barcode (124) as the reference.
  3. Procedure according to Claim 2 , where the dimension of the barcode (124) is a length of the barcode (124), a length of an element part of the barcode (124) or a limit dimension of the barcode (124).
  4. Procedure according to Claim 2 , wherein determining the reference quantity comprises: identifying a boundary of the barcode (124) in the image; determining, based on the boundary, whether the barcode (124) in the image is in a geometrically aligned position; and, in response to the fact that the boundary is not in a geometrically aligned position, performing a geometric transformation on the boundary and determining the reference quantity from a geometrically transformed boundary.
  5. Procedure according to Claim 4 , where the geometric transformation is a geometric rotation of the boundary, a geometric translation of the boundary, a geometric size change of the boundary and/or a geometric slope correction at the boundary.
  6. Procedure according to Claim 4 , furthermore, the determination of a dimension of a visual feature of the object (152) located outside the barcode (124) from the geometric transformation.
  7. Procedure according to Claim 1 , wherein determining the reference quantity comprises: determining a type of barcode (124); and determining a dimension of the barcode (124) from the type of barcode (124), wherein the dimension is the reference quantity associated with the barcode (124).
  8. Procedure according to Claim 7 , wherein the barcode type (124) is selected from the group consisting of 80% UPC, 100% UPC, a QR code, a 1D barcode (124), a 2D barcode (124), a Digimarc and a 2D data matrix.
  9. Procedure according to Claim 1 , wherein the visual feature is an edge of the object (152), an edge of a label containing at least part of the barcode (124), or a graphic on the object (152).
  10. Procedure according to Claim 9 , wherein the reference is a dimension of the barcode (124), and wherein comparing the reference with the visual feature includes: determining a geometric distance between the reference and the visual feature.
  11. Procedure according to Claim 9 , where the graphic on object (152) is text on object (152).
  12. Procedure according to Claim 1 , wherein the visual feature is a curvature of the object (152), and wherein comparing the reference with the visual feature includes: comparing a dimension of the barcode (124) with the curvature of the object (152).
  13. Procedure according to Claim 1 , wherein determining the reference quantity associated with the barcode (124) includes: determining a density of the barcode (124); and determining a dimension of the barcode (124) from the barcode density.
  14. Procedure according to Claim 1 , furthermore comprehensively: in response to the determination that the detection event of an impermissible object (152) has occurred, transmitting an alarm signal.
  15. Procedure according to Claim 1 , furthermore comprehensively: in response to determining the at least one dimension property, determining whether there is at least one reference dimension property of the object (152) to compare with the at least one dimension property, and in response to determining that there is no at least one reference dimension property to compare, storing the at least one dimension property in a reference dimension property model for the object (152).
  16. Procedure according to Claim 1 , furthermore, encompassing, prior to comparing the at least one dimensional property with at least one reference dimension property of the object (152): identifying and decoding a payload of the barcode (124); identifying the at least one reference dimension property from the decoded payload; and determining that the at least one dimensional property corresponds to the at least one reference dimension property.
  17. Procedure according to Claim 1 , wherein the at least one dimension property includes at least one of: an outer dimension of the object (152); a position of a label on the object (152); a position of the text on the object (152); and an internal dimension of the object (152).
  18. Procedure according to Claim 1 , further comprising: detecting a character near the barcode (124); determining a position of the character based on the reference size; and using an optical character recognition (OCR) technique for at least one of: storing the character and the position in a reference model; and comparing the character data with similarly positioned character data stored in a reference model.
  19. Procedure according to Claim 1 , wherein the at least one reference dimension property is part of a reference model, and wherein the method further comprises: checking the reference model for a visual feature of the reference model; examining the image of the object (152) for the visual feature, and not finding the visual feature of the reference model in the examined image; and, in response to not finding the visual feature of the reference model in the examined image, triggering an alarm.
  20. Procedure according to Claim 1 , wherein each of the steps is performed by a barcode reader (106).

Description

BACKGROUND Image-based scanners are frequently used to read barcodes at the point of sale (POS). In this context, the scanner can read a barcode to charge a customer a specific amount of money. However, thieves have developed a way to circumvent this by transferring the barcode from one product to another. This practice is sometimes called "ticket switching." Various applications for preventing ticket swaps and verifying items can be implemented using a convolutional neural network (CNN). For example, a CNN can classify an object in an image captured at the point of sale (POS) and then compare it to what the object "should" be based on information from a barcode classification on the object, thus determining whether a ticket swap has occurred. However, many CNN-driven applications are not easy to implement. They require additional processing beyond that of a typical decoding processor. This translates to extra time and cost. Furthermore, CNNs require a large number of images for training, typically over 10,000. Ideally, the training images are selected from the best images containing only one object. These images can be very large, requiring additional storage space. Furthermore, if a universal CNN is to be set up for identifying objects at all points of sale, these images are sent to a central database in the cloud. The number of images then transferred to the cloud puts a strain on a business's existing IT infrastructure. Additionally, further delays can occur if the processing takes place remotely in a cloud. CN 110 622 173 A This describes a method for detecting a mislabeled product. When a product's Maximum Retail Value (MRL) is scanned, an image of the product is captured. After the product is identified in the image, its size, including its area, can be calculated. If the size of the area containing the product does not match the standard MRL size, it can then be determined whether the MRL is incompatible with the product in the image. US 2017 / 0 193 430 A1 This describes a system and method for identifying products that need to be restocked on shelves, and in particular the use of markers captured by image data to determine the size of the empty shelf space. To mark areas on the shelves that are empty and need to be restocked, markers with a pattern are placed on or near the shelves that contain products. A camera captures an image of the shelf, the products, and the price tags. A processor running software is used to analyze the image and identify the markers and the shelf. Subsequently, the storage areas on the shelf are analyzed to identify empty areas characterized by lower light intensity compared to the adjacent areas. The processor determines the location of the empty areas and calculates their size based on the known marker size and the marker size in the image to generate a scale that is applied to the empty area. Using this information and the stored product data, the system determines which products should be restocked. US 2016 / 0 370 220 A1 This describes systems and procedures for calibrating a volume dimensioner. A calibration system comprises a dimensioning device and a reference object. The dimensioning device is configured to remotely detect the properties of an object and calculate its physical dimensions from these properties. The reference object has predefined physical dimensions and an outer surface with a pattern of reference markings. The dimensioning device is configured to calibrate itself using the reference object as a basis for comparison. EP 3 154 015 A1 describes a computer-implemented method, a computer program product, and a device for authenticating a visual identifier arranged on an article based on at least one image, wherein the article has at least one reference feature independent of the identifier and a three-dimensional object feature that deviates from a plane, wherein the authentication is based on the determination and comparison of a relative arrangement between the detected reference feature and the detected visual identifier, as well as on the detection of a three-dimensionality of the three-dimensional object feature and the verification of the detected three-dimensionality. Therefore, there is a need for improved techniques to detect and prevent ticket switching. DESCRIPTION According to the invention, a method with the features of claim 1, a method with the features of claim 21, and an electronic device with the features of claim 28 are provided. According to the invention, a method for determining object dimensions using a barcode reader is provided. The method comprises identifying a barcode in an image of an object; determining a reference dimension associated with the barcode; identifying a physical feature of the object located outside the barcode; comparing the reference dimension associated with the barcode with the physical feature and, in response, determining at least one dimensional property of the object; and, in response to determining