Amazon Technologies, Inc.

Pahal Kamlesh Dalal

Nishitkumar Ashokkumar Desai

Jayakrishnan Kumar Eledath

Gerard Guy Medioni

Jean Laurent Guigues

Alessandro Bergamo

Kaustav Kundu

Marian Nasr Amin George

Connor Spencer Blue Worley

Behjat Siddiquie

Petko Tsonev

Robert Matthias Steele

Hoi Cheung Pang

Kartik Muktinutalapati

Claire Law

Chuhang Zou

Rakesh Ramakrishnan

Lu Xia

Geoffrey A Franz

Paul Ignatius Dizon Echevarria

Matthew Fletcher Harrison

Sudarshan Narasimha Raghavan

Daniel Bibireata

Emilio Ian Maldonado

David Allen Smith

Nikhil Chacko

Min Xu

Kevin Kar Wai Lai

Jingjing Liu

Keshav Nand Tripathi

This disclosure describes techniques for updating planogram data associated with a facility. The planogram may indicate inventory locations within the facility for various types of items supported by product fixtures. In particular an image of a product fixture is analyzed to identify image segments corresponding to product groups, where each product group consists of instances of the same product and each image segment corresponds to a group of image points. Image data is further analyzed to determine coordinates of the points of each image segment. A product space corresponding to the product group is then defined based on the coordinates of the points of the product group. In some cases, for example, a product space may be defined in terms of the coordinates of the corners of a rectangular bounding box or volume.

Image-based detection of planogram product spaces

Commercial interactions with non-discretized items such as liquids in carafes or other dispensers are detected and associated with actors using images captured by one or more digital cameras including the carafes or dispensers within their fields of view. The images are processed to detect body parts of actors and other aspects therein, and to not only determine that a commercial interaction has occurred but also identify an actor that performed the commercial interaction. Based on information or data determined from such images, movements of body parts associated with raising, lowering or rotating one or more carafes or other dispensers may be detected, and a commercial interaction involving such carafes or dispensers may be detected and associated with a specific actor accordingly.

Detecting interactions with non-discretized items and associating interactions with actors using digital images

This disclosure describes systems and techniques for identifying events that occur within an environment using image data captured at the environment. For example, one or more cameras may generate image data representative of a user interacting with an item on the shelf. This image data may be used to generate feature data associated with the user and the item, which may be analyzed by one or more classifiers for identifying an interaction between the user and the item. The systems and techniques may then generate interaction data, which in turn may be analyzed by one or more additional classifiers for identifying an event, such as the user picking a particular item from the shelf within the environment. Event data indicative of the event may then be used to update a virtual cart of the user.

System and method for vision-based event detection

In a materials handling facility, events may be associated with users based on imaging data captured from multiple fields of view. When an event is detected at a location within the fields of view of multiple cameras, two or more of the cameras may be identified as having captured images of the location at a time of the event. Users within the materials handling facility may be identified from images captured prior to, during or after the event, and visual representations of the respective actors may be generated from the images. The event may be associated with one of the users based on distances between the users' hands and the location of the event, as determined from the visual representations, or based on imaging data captured from the users' hands, which may be processed to determine which, if any, of such hands includes an item associated with the event.

Associating items with actors based on digital imagery

Associating events with actors based on digital imagery

Described is a multiple-camera system and process for disambiguating between multiple users and identifying which of the multiple users performed an event. For example, when an event is detected, user patterns near the location of the event are determined, along with touch points at the location of the event. User pattern orientation and/or arm trajectories between the event location and the user patterns may be determined and processed to disambiguate between multiple users and determine which user pattern is involved in the event.

Disambiguating between multiple users

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Seattle, WA, United States of America

Pahal Kamlesh Dalal