7-eleven, Inc.

Kyle Dalal

Shahmeer Ali Mirza

Sailesh Bharathwaaj Krishnamurthy

Xinan Wang

Jon Andrew Crain

Crystal Maung

Matthew Raymond Magee

Fahad Mirza

Ryan Jerman Jones

An object tracking system includes a sensor and a controller. The sensor captures frames for a global plane for at least a portion of a space. The controller has a memory that stores a homography associated with the sensor and a processor. The processor receives a first frame from the sensor and identifies a pixel location within the first frame. The processor determines an estimated sensor location for the sensor by applying the homography to the pixel location, and determines a location difference between the estimated sensor location and an actual sensor location for the sensor. The processor then compares the location difference to a difference threshold level, and recomputes the homography in response to determining that the location difference exceeds the difference threshold level.

Homography error correction

An object tracking system that includes a first sensor and a second sensor that are each configured to capture frames of at least a portion of a global plane for a space. The system is configured to identify a first pixel location for a marker within a first frame and to determine an (x,y) coordinate for the marker using a first homography. The system is further configured to identify a second pixel location for the marker in the second sensor using a second homography, to identify a third pixel location using a disparity mapping, and to determine a distance difference between the second pixel location and the third pixel location. The system is further configured to compare the distance difference to a difference threshold level and to recompute the first homography and/or the second homography in response to determining that the distance difference exceeds the difference threshold level.

Homography error correction using a disparity mapping

An object tracking system that includes a first sensor and a second sensor that are each configured to capture frames of at least a portion of a global plane for a space. The system is configured to identify a pixel location for a marker within a frame from the first sensor and to determine an (x,y) coordinate for the marker using a first homography. The system is further configured to identify a pixel location for a different marker in a frame from the second sensor and to determine an (x,y) coordinate for the marker using a second homography. The system is further configured to determine a distance difference between the computed distance between the (x,y) coordinates and an actual distance. The system is further configured to recompute the first homography and/or the second homography in response to determining that the distance difference exceeds a difference threshold level.

Homography error correction using sensor locations

An image sensor is positioned such that a field-of-view of the sensor encompasses portion of a beverage machine. The field-of-view includes a first zone associated with operating the beverage machine to dispense the beverage and a second zone in which a cup is placed to receive the beverage. A beverage assignment subsystem receives angled-view images from the image sensor. An event associated with an object entering one or both of the first zone and the second zone is detected. In image(s) associated with a start of the event, it is determined that both a hand of a person enters the first zone and the cup is placed in the second zone. If the cup remained in the second zone for at least a threshold time, the beverage is assigned to the person whose hand entered the first zone.

Image-based self-serve beverage detection and assignment

A sensor calibration system configured to receive a first frame of one or more markers on a repositionable platform at a first location within a space from a sensor. The system is further configured to determine pixel locations in the first frame for a first marker and a second marker from among the one or more markers. The system is further configured to receive distance information that corresponds with a distance between the platform and distance measuring devices. The system is further configured to determine (x,y) coordinates for the first marker and the second marker based on the distance information. The system is further configured to generate a homography based on the (x,y) coordinates and pixel locations of the first marker and the second marker. The homography includes coefficients that translate between pixel locations in the first frame of the sensor and (x,y) coordinates in the global plane.

Draw wire encoder based homography

A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

System and method for position tracking using edge computing

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 and to identify a first pixel location and a second pixel location within the first frame. The system is further configured to determine (x,y) coordinates by applying a homography to the first pixel location and the second pixel location. The system is further configured to determine an estimated distance between the (x,y) coordinates, to determine an actual distance, and to determine a distance difference between the estimated distance and the actual distance. The system is further configured to compare the distance difference to a difference threshold level and to recompute the homography in response to determining that the distance difference exceeds the difference threshold level.

Homography error correction using marker locations

An item position tracking system includes weight sensors each associated with a weight board. Each weight sensor transmits sensor data indicative of a weight of an item to its corresponding weight board. Each weight board is configured to assign a particular address number to its corresponding weight sensor. The weight boards transmit the sensor data and the address numbers to a circuit board that transmits the sensor data and the address numbers to a weight server. The weight server determines from which weight sensor data is originated based on the address numbers, and whether items were removed from the weight sensors.

Mapping wireless weight sensor array for item detection and identification

An apparatus includes a memory and processor. The memory stores a machine learning algorithm configured to decide whether to use a virtual shopping cart to verify all or a portion of a transaction performed with an algorithmic shopping cart. The processor receives feedback for a decision made by the algorithm, indicating whether the algorithmic and virtual carts match. The processor assigns a reward to the feedback. A first positive reward is assigned when the virtual shopping cart is not used for verification, and the feedback indicates that the carts match. A second positive reward is assigned when the virtual cart is used for verification, and the feedback indicates that the carts do not match. A first negative reward is assigned when the virtual shopping cart is not used for verification, and the feedback indicates that the carts do not match. A second negative reward is assigned when the virtual cart is used for verification, and the feedback indicates that the carts match. The processor uses the reward to update the algorithm.

Machine learning algorithm trained to identify algorithmically populated shopping carts as candidates for verification

An apparatus includes a memory and processor. The memory stores a machine learning algorithm configured to decide between using an algorithmic and a virtual cart to process a transaction. The processor receives feedback for a decision made by the algorithm, indicating whether the algorithmic and virtual carts match. The processor assigns a reward to the feedback. A first positive reward is assigned when the algorithmic cart is selected, and the feedback indicates that the carts match. A second positive reward is assigned when the virtual cart is selected, and the feedback indicates that the carts do not match. A first negative reward is assigned when the algorithmic cart is selected, and the feedback indicates that the carts do not match. A second negative reward is assigned when the virtual cart is selected, and the feedback indicates that the carts match. The processor uses the reward to update the algorithm.

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Coppell, TX, United States of America

Kyle Dalal