SYSTEM AND METHOD FOR IDENTIFYING MISPLACED PRODUCTS IN A SHELF MANAGEMENT SYSTEM
Disclosed herein is a system and method of identifying misplaced products on a retail shelf using a feature extractor trained to extract features from images of products on the shelf and output identifying information regarding the product in the product image. The extracted features are compared to extracted features in a product library and a best fit is obtained. A misplaced product is identified if the identifying information produced by the feature extractor fails to match the identifying information associated with the best fit features from the product library.
1 . A computer-implemented method comprising:
obtaining an image containing one or more objects;
extracting an individual object image from a region of interest in the image;
extracting features from the object image using a trained feature extractor;
determining a best-fit match between features extracted from the object image and features associated with objects in an object library; and
determining an association between the best-fit match and the region of interest;
wherein the method is implemented in software executing on a processor; and
wherein the trained feature extractor is a deep neural network trained to on a dataset comprising multiple views of objects and associated identifying information of the objects.
2 . The method of claim 1 further comprising:
obtaining identifying information associated with the region of interest in the image; and
obtaining an identifier associated with the best fit match;
wherein the association between the best fit match and the region of interest is determined by a comparison of the identifier associated with the best fit match and the identifying information associated with the region of interest.
3 . The method of claim 2 further comprising:
determining that the comparison of the identifier associated with the best fit match and the identifying information associated with the region of interest indicates that the best fit match does not match with the object in the object image; and
indicating that the object in the object image is not associated with the other objects in the region of interest.
4 . The method of claim 1 wherein the method is repeated for each object image detected in the image.
5 . The method of claim 1 wherein the object library is built by a method comprising:
obtaining one or more source images and identifying information for each object;
acquiring multiple images of each object from a plurality of sources;
ranking the acquired images based on a highest confidence in an association between the acquired images and the source image;
selecting a pre-determined number of top-ranked acquired images; and
storing features extracted from the source image and the top-ranked acquired images, and the identifying information associated with the source image and the top-ranked acquired images, in the object library.
6 . The method of claim 5 wherein the acquired multiple images for each object include images exhibiting different variations and/or viewpoints for each object.
7 . The method of claim 5 wherein the multiple images of each object include images of the object exhibiting pose variations.
8 . The method of claim 5 wherein the multiple images of each object include images of the object exhibiting variations in labelling of the object.
9 . The method of claim 5 wherein the multiple images of each object include images of the object associated with different identifying information.
10 . The method of claim 1 wherein the feature extractor is trained on a dataset comprising multiple images of each object and associated identifying information.
11 . The method of claim 1 wherein the feature extractor outputs the identifying information, given an image of the object as input.
12 . The method of claim 1 further comprising:
determining that the identifier associated with an object image does not exist in the object library, indicating a new object; and
enrolling features extracted from the object image and the associated identifier in the object library.
13 . The method of claim 1 further comprising:
determining that the features extracted from the object image are not a best fit with the features in the object library;
determining that the identifier associated with an object image does exist in the object library;
determining that the object image represents an existing object with new labelling; and
associating features extracted from the object image with the existing identifying information in the object library.
14 . The method of claim 1 further comprising:
determining that the best fit match matches identifying information from an adjacent region of interest; and
identifying the individual object as a spread.
15 . A system comprising:
a camera, for obtaining images containing a plurality of objects;
a processor, executing software for analyzing the images;
a feature extractor trained to extract features from an object in the image and output identifying information associated with the object; and
an object library containing features extracted from multiple objects within the image, the features associated with identifying information of the object;
wherein the software performs the functions of:
obtaining an image containing one or more objects;
extracting an individual object image from a region of interest in the image;
extracting features from the object image using a trained feature extractor;
determining a best-fit match between features extracted from the object image and features associated with objects in an object library; and
determining an association between the best-fit match and the region of interest;
wherein the trained feature extractor is a deep neural network trained to on a dataset comprising multiple views of objects and associated identifying information of the objects.
16 . The method of claim 15 , the software performing the further functions of:
obtaining identifying information associated with the region of interest in the image; and
obtaining an identifier associated with the best fit match;
wherein the association between the best fit match and the region of interest is determined by a comparison of the identifier associated with the best fit match and the identifying information associated with the region of interest.
17 . The method of claim 16 the software performing the further functions of:
determining that the comparison of the identifier associated with the best fit match and the identifying information associated with the region of interest indicates that the best fit match does not match with the object in the object image; and
indicating that the object in the object image is not associated with the other objects in the region of interest.
18 . The system of claim 15 wherein the object library is built by:
obtaining a source image and identifying information for each object;
acquiring multiple images of each object from a plurality of sources;
ranking the acquired images based on a highest confidence in an association between the acquired images and the source image;
selecting a pre-determined number of the top-ranked acquired images; and
storing features extracted from the source image and the top-ranked acquired images, and the identifying information associated with the source image and the top-ranked acquired images, in the object library.
19 . The system of claim 18 wherein the acquired multiple images for each object include images exhibiting different variations and/or viewpoints for each object.
20 . The method of claim 15 wherein the feature extractor outputs the identifying information, given an image of the object as input.