7-eleven, Inc.

Madan Mohan Chinnam

Shahmeer Ali Mirza

Sailesh Bharathwaaj Krishnamurthy

Sarath Vakacharla

Trong Nghia Nguyen

Caleb Austin Boulio

Crystal Maung

Deepanjan Paul

Fahad Mirza

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 processor that tracks first and second objects in a space. Upon detecting that a tracked position of the first object is within a threshold distance of a tracked position of a second object, a top-view image of the first object is received from a first sensor. Based on this top-view image, a first descriptor is determined for the first object. The first descriptor is associated with an observable characteristic of the first object. The processor identifies the first object based at least in part upon the first descriptor.

Object re-identification during image tracking

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.

An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system is configured to receive a first frame from the sensor, to identify a pixel location within the first frame, and to determine an estimated sensor location for the sensor by applying a homography to the pixel location. The homography includes coefficients that translate between pixel locations in a frame from the sensor and (x,y) coordinates in the global plane. The system is further configured to determine an actual sensor location for the sensor and to determine a location difference between the estimated sensor location and the actual sensor location. The system is further configured to compare the location difference to a difference threshold level and to recompute the homography in response to determining that the location difference exceeds the difference threshold level.

Homography error correction

A system includes sensors and a tracking subsystem. The subsystem tracks a first object and one or more other objects in a space. After determining that re-identification of the first object is needed, candidate identifiers are determined for the first object. The candidate identifiers include a subset of the identifiers of all tracked objects in the space. The subset includes possible identifiers of the first object based on a history of movements of the first object and interactions of the first object with the other objects in the space. Based on a top-view image, a first descriptor is determined for the first object. The first descriptor is associated with a characteristic of the first object. Based on results of comparing the first descriptor to a set of predetermined descriptors, an updated identifier is determined for the first object.

Vector-based object re-identification during image tracking

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 people in the captured videos, and a camera server that determines the physical positions of people in the space based on the determined local coordinates. The weight subsystem determines when items were removed from shelves. The central server determines which person in the space removed the items based on the physical positions of the people in the space and the determination of when items were removed.

A system includes sensors and a tracking subsystem. The subsystem tracks first and second objects in a space. Following a collision event between the first and second object, a top-view image of the first object is received from a first sensor. Based on the top-view image, a first descriptor is determined for the first object. The first descriptor is associated with an observable characteristic of the first object. If criteria are not satisfied for distinguishing the first object from the second object based on the first descriptor, a third descriptor is determined for the first object. The third descriptor is generated by an artificial neural network configured to identify objects in top-view images. The tracking subsystem uses the third descriptor to assign an identifier to the first object.

A system includes sensors and a tracking subsystem. The subsystem tracks a first object and one or more other objects in a space. After determining that re-identification of the first object is needed, candidate identifiers are determined for the first object The candidate identifiers include a subset of the identifiers of all tracked objects in the space. The subset includes possible identifiers of the first object based on a history of movements of the first object and interactions of the first object with the other objects in the space. Based on a top-view image, a first descriptor is determined for the first object. The first descriptor is associated with a characteristic of the first object. Based on results of comparing the first descriptor to a set of predetermined descriptors, an updated identifier is determined for the first object.

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Irving, TX, United States of America

Madan Mohan Chinnam