Target Brands, Inc.

Nicholas Scott Eggert

Ryan David Siskind

Corey Hadden

Rachel Alderman

Edgar Cobos

Stephen Radachy

Matthew Nokleby

Shalin Shah

Ramasubbu Venkatesh

Tapan Shah

Jamie Barras

Ramanan Muthuraman

Methods and systems for attributing browsing activity from two or more different network-connected devices to a single user are disclosed. In one aspect, cookies generated by the browsing activity of different unidentified devices at a website are received. A random forest classifier trained on probabilities output from a Gaussian mixture model is applied to the unidentified cookies to determine a probability that two different cookies were generated by the same user. In some embodiments, personalized content is then delivered to the user based on the characteristics of the paired cookies.

Similarity learning-based device attribution

The disclosed system and method relate to automatically detecting empty spaces on retail store shelves, identifying the missing product(s) and causing the space to be replenished or restocked. For example, stores may use shelf-mounted imaging devices to capture images of shelves across the aisle from the imaging devices. The images captured by the imaging devices may be pre-processed to de-warp, de-skew images and stitch together multiple images in order to retrieve an image that captures a full width of a shelf. The pre-processed images can then be used to detect products on the shelf, identify the detected products. An iterative projection algorithm or product fingerprint matching algorithm can be used to identify the products. When an incorrect product listing or an empty shelf space is encountered, a message may be sent to the store employee to remedy the issue.

Retail shelf image processing and inventory tracking system

A system may track a person at a location across multiple cameras in real time, while maintaining identification of the person through their visit. The person may not be personally identified; however, the person's presence in video frames captured by different cameras may be linked together. The linked frames may be evaluated by one or more analytics applications. For example, an analytics application may determine a trajectory of a person through a location and plot an overlay of that person's trip through the location on the map. This information may be used, for example, to optimize store layout, asset protection purposes, for counting a number of people at the location at a given time (e.g., for understanding busy hours of the location), determining whether social distancing or other health and/or safety measures may be in compliance, etc.

Multi-camera person re-identification

Systems and methods for automating image annotations are provided, such that a large-scale annotated image collection may be efficiently generated for use in machine learning applications. In some aspects, a mobile device may capture image frames, identifying items appearing in the image frames and detect objects in three-dimensional space across those image frames. Cropped images may be created as associated with each item, which may then be correlated to the detected objects. A unique identifier may then be captured that is associated with the detected object, and labels are automatically applied to the cropped images based on data associated with that unique identifier. In some contexts, images of products carried by a retailer may be captured, and item data may be associated with such images based on that retailer's item taxonomy, for later classification of other/future products.

Large-scale automated image annotation system

Methods and systems for identifying one or more products in an electronic image are disclosed. The computer-implemented method to electronically recognize a product in an electronic image captured via an electronic mobile device is disclosed. The method includes receiving, by a server, a video stream from a camera of the electronic mobile device, the video stream including a plurality of frames. The server selects at least one of the plurality of frames from the video stream, the at least one of the plurality of frames from the video stream being selected is a captured image. The server segments a plurality of products in the captured image into a plurality of segments. The server performs an image recognition using each of the plurality of segments to identify the product in each of the plurality of segments. One or more recognized products identified in the image recognition is outputted by the server.

Electronic product recognition

A retail store including a server having a processor and a memory; a communication network; and a database are disclosed. The server includes an electronic product recognizer that receives a video stream including a plurality of frames from a camera of an electronic mobile device. At least one of the plurality of frames is selected as a captured image. A plurality of products in the captured image is segmented into a plurality of segments. Image recognition is performed using each of the plurality of segments to identify the product in each of the plurality of segments. One or more recognized products identified in the image recognition are output. The one or more recognized products identified in the image recognition are configured to be sent to a user device communicable with the server via the communication network, the server configured to cause one or more stickers to be displayed on the user device.

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Inver Grove Heights, MN, United States of America

Nicholas Scott Eggert