System and method for computer vision driven applications within an environment
First Claim
1. A method for expediting a checkout process comprising:
- collecting image data across an environment;
maintaining an environmental object graph from the image data wherein the environmental object graph is a data representation of computer vision classified objects in space and time across the environment, the environmental object graph comprising at least a subset of objects having object associations;
wherein maintaining the environmental object graph comprises at least one instance of;
in a first region captured in the image data, classifying a first object and at least a shopper object,in the first region, detecting an interaction event between the first object and the shopper object, andupdating the environmental object graph whereby the first object is associated with the shopper object;
inspecting objects that are associated with the shopper object and thereby generating a checkout list.
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Abstract
A system and method for computer vision driven applications in an environment that can include collecting image data across an environment; maintaining an environmental object graph from the image data whereby maintaining the environmental object graph is an iterative process that includes: classifying objects, tracking object locations, detecting interaction events, instantiating object associations in the environmental object graph, and updating the environmental object graph by propagating change in at least one object instance across object associations; and inspecting object state for at least one object instance in the environmental object graph and executing an action associated with the object state. The system and method can be applied to automatic checkout, inventory management, and/or other system integrations.
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Citations
44 Claims
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1. A method for expediting a checkout process comprising:
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collecting image data across an environment; maintaining an environmental object graph from the image data wherein the environmental object graph is a data representation of computer vision classified objects in space and time across the environment, the environmental object graph comprising at least a subset of objects having object associations; wherein maintaining the environmental object graph comprises at least one instance of; in a first region captured in the image data, classifying a first object and at least a shopper object, in the first region, detecting an interaction event between the first object and the shopper object, and updating the environmental object graph whereby the first object is associated with the shopper object; inspecting objects that are associated with the shopper object and thereby generating a checkout list. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A method comprising:
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collecting image data across an environment; maintaining an environmental object graph from the image data whereby maintaining the environmental object graph is an iterative process comprising; classifying objects and storing corresponding object instances in the environmental object graph, tracking object locations and establishing an association of object instances in an object path, detecting interaction events and, for event instances of a subset of detected interaction events, generating an object association of at least two object instances involved in the interaction event, and updating the environmental object graph comprising propagating change in at least one object instance across object associations; and inspecting object state for at least one object instance in the environmental object graph and executing an action associated with the object state. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35)
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36. A system comprising
an imaging system configured to collect image data within an environment; -
a processing engine configured to maintain an environmental object graph from the image data wherein the environmental object graph is a data representation of classified objects in space and time across the environment, the environmental object graph comprising at least a subset of objects having probabilistic object associations; and the processing engine further configured to; classify objects in the image data and store corresponding object instances in the environmental object graph, track object locations and establish an association of object instances in an object path, detect interaction events and, for event instances of a subset of detected interaction events, generate an object association of at least two object instances involved in the interaction event, and update the environmental object graph and propagate change in at least one object instance across object associations. - View Dependent Claims (37, 38, 39, 40, 41, 42, 43, 44)
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Specification