A method for expediting a checkout process comprising: collecting image data across an environment from multiple image capture devices distributed at distinct points in the environment; maintaining an environmental object graph "EOG" 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 probabilistic object associations, wherein maintaining the environmental object graph during a first instance comprises: 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 probabilistically associated with the shopper object as a possessed object with a first confidence level, wherein maintaining the environmental object graph during a second instance comprises: in a second region captured in the image data, classifying a second object and the shopper object, associating the second object as a possessed object with the shopper object with a second confidence level, and updating the EOG wherein the first confidence level is altered at least partially in response to the second confidence level, wherein maintaining the environmental object graph during a third instance after the first instance comprises: in the first region, classifying the first object, and updating the environmental object graph and thereby removing the association of the first object with the shopper object in the first instance; inspecting objects that are probabilistically associated with the shopper object and thereby generating a checkout list; and in a third region captured in the image data, detecting the shopper object and initiating a checkout process with the checkout list. collecting image data across an environment from multiple image capture devices distributed at distinct points in the environment; maintaining an environmental object graph "EOG" 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 probabilistic object associations, wherein maintaining the environmental object graph during a first instance comprises: 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 probabilistically associated with the shopper object as a possessed object with a first confidence level, 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 probabilistically associated with the shopper object as a possessed object with a first confidence level, wherein maintaining the environmental object graph during a second instance comprises: in a second region captured in the image data, classifying a second object and the shopper object, associating the second object as a possessed object with the shopper object with a second confidence level, and updating the EOG wherein the first confidence level is altered at least partially in response to the second confidence level, in a second region captured in the image data, classifying a second object and the shopper object, associating the second object as a possessed object with the shopper object with a second confidence level, and updating the EOG wherein the first confidence level is altered at least partially in response to the second confidence level, wherein maintaining the environmental object graph during a third instance after the first instance comprises: in the first region, classifying the first object, and updating the environmental object graph and thereby removing the association of the first object with the shopper object in the first instance; in the first region, classifying the first object, and updating the environmental object graph and thereby removing the association of the first object with the shopper object in the first instance; inspecting objects that are probabilistically associated with the shopper object and thereby generating a checkout list; and in a third region captured in the image data, detecting the shopper object and initiating a checkout process with the checkout list.