7-eleven, Inc.

Trong Nghia Nguyen

Shahmeer Ali Mirza

Sarath Vakacharla

Sailesh Bharathwaaj Krishnamurthy

Caleb Austin Boulio

Matthew Raymond Magee

Madan Mohan Chinnam

Deepanjan Paul

Ravi Kumar Kurva

Joshua E Berry

Maninder Singh Suri

Fahad Mirza

Ranganathan Mohan

Jonathan Christopher Hodge

Franklin Philips

Crystal Maung

An apparatus to create a virtual layout of a virtual store to emulate a physical layout of a physical store includes a memory and a processor. The processor receives a physical position and orientation associated with a physical rack located in the physical store. In response, the processor places a virtual rack at a virtual position and orientation on the virtual layout, to represent the physical position and orientation of the physical rack on the physical layout. The processor receives virtual items associated with physical items located on physical shelves of the physical rack. In response, the processor places the virtual items on virtual shelves of the virtual rack, the virtual shelves representing the physical shelves of the physical rack. The processor assigns a rack camera, which captures video that includes the physical rack, to the virtual rack and stores the virtual layout in the memory.

Tool for generating a virtual store that emulates a physical store

An apparatus includes a display, interface, and processor. The interface receives video from a camera located in a physical store and directed at a first physical rack. The camera captures video of the rack during a shopping session. The processor displays a first virtual rack that emulates the first physical rack and includes first and second virtual shelves. The virtual shelves include virtual items, which include graphical representations of physical items located on the physical rack. The processor displays the rack video, which depicts an event including the person interacting with the first physical rack. The processor also displays a virtual shopping cart. The processor receives information associated with the event, identifying the first virtual item. The rack video depicts that the person selected the first physical item while interacting with the first physical rack. The processor then stores the first virtual item in the virtual shopping cart.

System and method for populating a virtual shopping cart based on video of a customer's shopping session at a physical store

An apparatus includes a processor. The processor receives an algorithmic shopping cart that includes a first set of items determined by an algorithm to have been selected by a person during a shopping session in a physical store, based on a set of inputs received from sensors located within the physical store. The processor also receives a virtual shopping cart that includes a second set of items. Video of the shopping session was captured by a set of cameras located in the physical store and depicts the person selecting the second set of items. The processor compares the algorithmic cart to the virtual cart and determines that a discrepancy exists between the algorithmic cart and the virtual cart. The processor determines a subset of the set of inputs associated with the discrepancy and attaches metadata explaining the discrepancy to the subset. The processor uses the subset to train the algorithm.

Feedback and training for a machine learning algorithm configured to determine customer purchases during a shopping session at a physical store

A scalable tracking system includes a camera subsystem, a weight subsystem, and a central server. The camera subsystem includes cameras that capture video of a space, camera clients that determine local coordinates of objects in the captured videos, and a camera server that determines the physical positions of objects in the space based on the determined local coordinates. The weight subsystem determines when items were removed from shelves. The central server determines which object in the space removed the items based on the physical positions of the objects in the space and the determination of when items were removed.

Scalable position tracking system for tracking position in large spaces

A system includes sensors and a tracking subsystem. The subsystem receives frames of top-view images generated by the sensors. The subsystem tracks a first and second object, based on received frames. The subsystem detects that the first object is within a threshold distance of the second object. In response, the subsystem determines a probability that the first object switched identifiers with the second object and updates candidate lists accordingly for the first and second objects. The updated first candidate list includes a probability that the first object is associated with a first identifier and a probability that the first object is associated with a second identifier.

Determining candidate object identities during image tracking

An apparatus includes a display, interface, and processor. The interface receives a live camera feed from a camera directed at a first physical structure located in a physical space. The processor receives an indication of an event associated with the first physical structure and accordingly displays a first virtual structure corresponding to the first physical structure. The first virtual structure includes virtual items that emulate physical items located on the first physical structure. The processor additionally displays a recording of the live camera feed, which depicts the event associated with the first physical structure.

System and method for presenting a virtual store shelf that emulates a physical store shelf

An object tracking system includes a sensor, a weight sensor, and a tracking system. The sensor is configured to capture a frame of at least a portion of a rack within a global plane for a space. The tracking system is configured to detect a weight decrease on the weight sensor. The tracking system is further configured to receive the frame of the rack, to determine a pixel location for a person, to determine the person is within a predefined zone associated with the rack. The tracking system is further configured to identify the item associated with the weight sensor and to add the identified item to a digital cart associated with the person.

Topview item tracking using a sensor array

A scalable tracking system processes video of a space to track the positions of objects within a space. The tracking system determines local coordinates for the objects within frames of the video and then assigns these coordinates to time windows based on when the frames were received. The tracking system then combines or clusters certain local coordinates that have been assigned to the same time window to determine a combined coordinate for an object during that time window.

Tracking positions using a scalable position tracking system

A scalable tracking system processes video of a space to track the positions of people within a space. The tracking system determines local coordinates for the people within frames of the video and then assigns these coordinates to time windows based on when the frames were received. The tracking system then combines or clusters certain local coordinates that have been assigned to the same time window to determine a combined coordinate for a person during that time window.

A system includes a sensor and a client. The client receives a set of frames of top-view depth images generated by the sensor. The client identifies a frame of the received frames in which a first contour associated with a first object is merged with a second contour associated with a second object. The client determines, at a first depth in the identified frame, a merged-contour region which is associated with the merged contours. The client detects a third contour at a second depth that is less than the first depth and determines a first region associated with the third contour. The client detects a fourth contour at the second depth and determines a second region associated with the fourth contour. If criteria are satisfied, the client associates the first region with a position of the first object and associates the second region with a position of the second object.

Contour-based detection of closely spaced objects

A camera array for a scalable tracking system includes cameras that are communicatively coupled to camera clients. The cameras are arranged in a grid such that no camera is directly adjacent in the same row or column of the grid to another camera that is communicatively coupled to the same camera client. Cameras that are arranged along a diagonal of the grid are communicatively coupled to the same camera client.

Sensor array for scalable position tracking system

An apparatus includes a display, interface, and processor. The interface receives a camera feed from a camera directed at a first physical rack located in a physical store. The processor displays virtual racks assigned to physical racks. The processor receives an indication of an event associated with the first physical rack and accordingly displays a first virtual rack, which includes first and second virtual shelves. The first and second virtual shelves include virtual items that emulate physical items located on physical shelves of the first physical rack. The processor additionally displays a recording of the camera feed, which depicts the event associated with the first physical rack.

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Dallas, TX, United States of America

Trong Nghia Nguyen