Toshiba Global Commerce Solutions

Toshiba Global Commerce Solutions Holdings Corporation

Smart Supervision System LLC

Yevgeni Tsirulnik

Charles Ray Kirk

J Wacho Slaughter

Evgeny Shevtsov

Andrei Khaitas

Anton Kramarov

Srija Ganguly

Brad Matthew Johnson

William L Dungan

Phil Brown

Yeshai Bouskila

The present disclosure provides techniques for tracking and verifying items that are scanned multiple times in a self-checkout system. A plurality of inputs indicating more than one scan of an item are received at a self-checkout machine. An image of a bagging area is captured. A CNN is trained to recognize the item based on a training dataset and a selected training algorithm. One or more units of the item are detected in the bagging area from the image using the CNN. A number of the detected units of the item in the bagging area is compared with a number of times the item has been scanned. Upon determining that there is no match, a shopping list displayed on the self-checkout machine is adjusted to reflect the number of the detected units of the item in the bagging area.

Item detection and cart validation system

Self-checkout kiosk

Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Computer vision grouping recognition system

Systems and methods of detecting, identifying, and classifying objects positioned on a shelf are provided. In one exemplary embodiment, a sensor pad comprises a plurality of electrical contacts disposed in the pad as a matrix of rows and columns of electrical contacts. Further, each contact corresponds to a different surface area of the pad and has a first electrical conductor positioned on a first layer and a second electrical conductor positioned opposite to the first conductor on a second layer. Also, at least one of the first and second conductors is operable to vary in resistance or capacitance based on an amount of pressure applied to that conductor. In addition, each contact is configured to enable an electrical connection between the first and second conductors with a resistance or capacitance that varies based on an amount of pressure applied to a corresponding area of the pad when an object is positioned on that pad.

Systems and methods of detecting, identifying and classifying objects positioned on a surface

Growing community of inventors

Frisco, TX, United States of America

Yevgeni Tsirulnik