Automatic mapping of store layout using soft object recognition

Patent metadata

Field	Value
Publication number	US-10991036-B1
Application number	US-202016883655-A
Country	US
Kind code	B1
Filing date	May 26, 2020
Priority date	May 22, 2015
Publication date	Apr 27, 2021
Grant date	Apr 27, 2021

Abstract

A method for automatically mapping a store layout includes identifying a path for traversing a retail area and capturing images of the retail area at various points along the path. The images may be analyzed to identify visual characteristics which may be compared to a template of retail products in a template library. When an object depicted in the image matches with a retail product in the template library, the object may correspond to the retail product. Additionally, a retail department for the object may also be identified. The retail department may be compared to the retail product corresponding to the object, and when the retail product is not associated with the retail department, another retail product which is associated with the retail department may be identified as corresponding to the object. A map of the store layout may be generated based on the identified retail products.

First claim

We claim:

1. A computer-implemented method for automatically mapping a store layout, the method executed by one or more processors programmed to perform the method, the method comprising:
receiving, at the one or more processors, a three-dimensional image depicting one or more merchandizing fixtures within a retail area;
identifying, by the one or more processors, visual characteristics within an object on the one or more merchandizing fixtures;
determining, by the one or more processors, likelihoods that the object corresponds to a plurality of retail products by comparing the visual characteristics of the object to visual characteristics for each of the plurality of retail products;
determining, by the one or more processors, distances from the object to retail products neighboring the object;
identifying, by the one or more processors, retail departments corresponding to the retail products neighboring the object;
determining, by the one or more processors, a retail department for the object based on the distances from the object to the retail products neighboring the object;
adjusting, by the one or more processors, the likelihoods that the object corresponds to the plurality of retail products by comparing the retail department for the object to retail departments for the plurality of retail products;
determining, by the one or more processors, a retail product for the object having a highest adjusted likelihood of the adjusted likelihoods that the object corresponds to the plurality of retail products; and
generating, by the one or more processors, a map of a store layout for the retail area including an indication of the determined retail product within the retail area.

2. The method of claim 1 , wherein determining a retail department for the object includes:
determining, by the one or more processors, the retail department for the object based on a most frequently occurring retail department of the retail departments.

3. The method of claim 1 , wherein determining a retail department for the object further includes:
identifying, by the one or more processors within the three-dimensional image, one or more tags corresponding to objects neighboring the object; and
analyzing, by the one or more processors, the one or more tags to identify retail departments.

4. The method of claim 1 , wherein identifying visual characteristics within an object includes:
identifying, by the one or more processors, boundaries of the object within the three-dimensional image; and
identifying, by the one or more processors, the visual characteristics within the boundaries of the object.

5. The method of claim 4 , wherein identifying visual characteristics within the boundaries of the object includes:
identifying, by the one or more processors, text characters within the object using stroke width transform (SWT) techniques; and
identifying, by the one or more processors, a text string based on the text characters using optical character recognition (OCR) techniques;
wherein determining likelihoods that the object corresponds to the plurality of retail products based on the visual characteristics of the object further includes:
comparing, by the one or more processors, the text string to text strings corresponding to templates of the plurality of retail products; and
determining, by the one or more processors, the likelihoods that the object corresponds to the plurality of retail products based on the comparison.

6. The method of claim 4 , wherein identifying visual characteristics within the boundaries the object includes:
identifying, by the one or more processors, a size and shape of the object;
wherein determining likelihoods that the object corresponds to the plurality of retail products based on the visual characteristics of the object further includes:
comparing, by the one or more processors, the size and shape of the object to templates of the plurality of retail products; and
determining, by the one or more processors, the likelihoods that the object corresponds to the plurality of retail products based on the comparison.

7. The method of claim 1 , wherein determining likelihoods that the object corresponds to the plurality of retail products based on the visual characteristics of the object further includes:
determining, by the one or more processors, a first likelihood that the object corresponds to one of the plurality of retail products by comparing a size and shape of the object to a size and shape for a template of the retail product;
determining, by the one or more processors, a second likelihood that the object corresponds to the retail product by comparing a text string in the object to a text string for the template of the retail product;
determining, by the one or more processors, a third likelihood that the object corresponds to the retail product by comparing style parameters for the object to style parameters for the template of the retail product; and
combining, by the one or more processors, the first, second, and third likelihoods to determine an overall likelihood that the object corresponds to the retail product.

8. The method of claim 1 , wherein determining a retail product for the object based on the likelihoods that the object corresponds to the plurality of retail products includes:
determining that the object corresponds to one of the plurality of retail products when at least one of: (i) the likelihood for the retail product exceeds a predetermined likelihood threshold or (ii) the likelihood for the retail product is a highest likelihood of the likelihoods for the plurality of retail products.

9. The method of claim 1 , further comprising:
filtering, by the one or more processors, the one or more merchandizing fixtures from the three-dimensional image including:
identifying depth within the three-dimensional image; and
filtering out one or more portions of the three-dimensional image having a depth which is less than a predetermined depth threshold.

10. The method of claim 1 , further comprising:
displaying, by the one or more processors, the map of the store layout on a user interface.

11. A system for automatically mapping a store layout, the system comprising:
one or more processors,
a non-transitory computer-readable memory coupled to the one or more processors, and storing thereon instructions that, when executed by the one or more processors, cause the system to:
receive a three-dimensional image depicting one or more merchandizing fixtures within a retail area;
identify visual characteristics within an object on the one or more merchandizing fixtures;
determine likelihoods that the object corresponds to a plurality of retail products by comparing the visual characteristics of the object to visual characteristics for each of the plurality of retail products;
determine distances from the object to retail products neighboring the object;
identify retail departments corresponding to the retail products neighboring the object;
determine a retail department for the object based on the distances from the object to the retail products neighboring the object;
adjust the likelihoods that the object corresponds to the plurality of retail products by comparing the retail department for the object to retail departments for the plurality of retail products;
determine a retail product for the object having a highest adjusted likelihood of the adjusted likelihoods that the object corresponds to the plurality of retail products; and
generate a map of a store layout for the retail area including an indication of the determined retail product within the retail area.

12. The system of claim 11 , wherein

Assignees

• Walgreen Co
• Univ Pennsylvania

Inventors

• Bergstrom John M
• Cleveland Jonas
• Thakur Dinesh
• Dames Philip
• Philips Cody
• Kientz Terry
• Daniilidis Kostas
• Kumar Vijay

Classifications

• G06V30/18

  Extraction of features or characteristics of the image · CPC title

• G06V30/10

  Character recognition · CPC title

• G06V20/10

  Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59) · CPC title

• G06F18/24147

  Distances to closest patterns, e.g. nearest neighbour classification · CPC title

• H04N23/60

  Control of cameras or camera modules · CPC title