Amazon Technologies, Inc.

Eli Osherovich

Gerard Guy Medioni

Dilip Kumar

Ehud Benyamin Rivlin

Gopi Prashanth Gopal

Paul Aksenti Savastinuk

George Leifman

Roman Talyansky

Michael Dillon

Yacov Hel-Or

Dmitri Veikherman

Described is a multiple-camera system and process for detecting, tracking, and re-verifying agents within a materials handling facility. In one implementation, a plurality of feature vectors may be generated for an agent and maintained as an agent model representative of the agent. When the object being tracked as the agent is to be re-verified, feature vectors representative of the object are generated and stored as a probe agent model. Feature vectors of the probe agent model are compared with corresponding feature vectors of candidate agent models for agents located in the materials handling facility. Based on the similarity scores, the agent may be re-verified, it may be determined that identifiers used for objects tracked as representative of the agents have been flipped, and/or to determine that tracking of the object representing the agent has been dropped.

Agent re-verification and resolution using imaging

This disclosure describes systems and techniques for detecting events, determining a result of each respective event using a first hypothesis source, and calculating a likelihood that a second (and/or additional) hypothesis source would determine the same result of the respective event. The calculated likelihood may then be used to be determine whether to request that the second hypothesis source determine the result of the event, determine an amount of resources of the second hypothesis source to use to make this determination, and/or like.

Generating event results using auto-generated sensor data

Growing community of inventors

Haifa, Israel

Eli Osherovich