Amazon Technologies, Inc.

Hoi Cheung Pang

Jayakrishnan Kumar Eledath

Gerard Guy Medioni

Nishitkumar Ashokkumar Desai

Dilip Kumar

Daniel Bibireata

Kushagra Srivastava

Jaechul Kim

Kartik Muktinutalapati

Shaonan Zhang

Behjat Siddiquie

Pahal Kamlesh Dalal

Tian Lan

Kaustav Kundu

Rakesh Ramakrishnan

Geoffrey A Franz

Where an event is determined to have occurred at a location within a vicinity of a plurality of actors, imaging data captured using cameras having the location is processed using one or more machine learning systems or techniques operating on the cameras to determine which of the actors is most likely associated with the event. For each relevant pixel of each image captured by a camera, the camera returns a set of vectors extending to pixels of body parts of actors who are most likely to have been involved with an event occurring at the relevant pixel, along with a measure of confidence in the respective vectors. A server receives the vectors from the cameras, determines which of the images depicted the event in a favorable view, based at least in part on the quality of such images, and selects one of the actors as associated with the event accordingly.

Associating events with actors using digital imagery and machine learning

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

As agents move about a materials handling facility, tracklets representative of the position of each agent are maintained along with a confidence score indicating a confidence that the position of the agent is known. If the confidence score falls below a threshold level, image data of the agent associated with the low confidence score is obtained and processed to generate one or more embedding vectors representative of the agent at a current position. Those embedding vectors are then compared with embedding vectors of other candidate agents to determine a set of embedding vectors having a highest similarity. The candidate agent represented by the set of embedding vectors having the highest similarity score is determined to be the agent and the position of that candidate agent is updated to the current position, thereby re-identifying the agent.

Re-identification of agents using image analysis and machine learning

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Hoi Cheung Pang