Abstract: 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.

Abstract: An object tracking system that includes a plurality of sensors and a tracking system. A first sensor from the plurality of sensors is configured to capture a first frame of a global plane for at least a portion of the space. The tracking system is configured to determine a pixel location in the first frame for an object located in the space, and to apply a homography to the pixel location to determine a coordinate in the global plane. The homography is configured to translate between pixel locations in the first frame and coordinates in the global plane.

Abstract: 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.

Abstract: A system includes sensors and a tracking subsystem. The subsystem receives a first image feed from a first sensor and a second image feed from a second sensor. The field-of view of the second sensor at least partially overlaps with that of the first sensor. The subsystem detects, in a frame from the first feed, a first contour associated with an object. The subsystem determines, based on pixel coordinates of the first contour, a first pixel position of the object. The subsystem detects, in a frame from the second feed, a second contour associated with the same object. The subsystem determines, based on pixel coordinates of the second contour, a second pixel position of the object. Based on the first pixel position and the second pixel position, a global position for the object is determined in a space.

Abstract: An object tracking system includes a first sensor, a second sensor, and a tracking system. The first sensor is configured to capture a first frame of a global plane for at least a first portion of a space. The second sensor is configured to capture a second frame of at least a second portion of the space. The tracking system is configured to determine the object is within an overlap region with the second sensor based on a first pixel location. The tracking system is further configured to determine a first coordinate in the global plane for the object, to determine a second pixel location in the second frame for the object based on the first coordinate, and to store the second pixel location with an object identifier a tracking list associated with the second sensor.

Abstract: A system includes a sensor, a weight sensor, and a tracking subsystem. The tracking subsystem receives an image feed of top-view images generated by the sensor and weight measurements from the weight sensor. The tracking subsystem detects an event associated with an item being removed from a rack in which the weight sensor is installed. The tracking subsystem determines that a first person and a second person may be associated with the event. In response, the tracking subsystem dilates contours associated with the first and second person from a first depth to a second depth until the contours enter a zone adjacent to the rack. A number of iterations is determined for each contour to enter the zone adjacent to the rack. If the first person's contour enters the zone in fewer iterations, the item is assigned to the first person.

Abstract: An apparatus includes an interface, display, memory, and processor. The interface receives a video feed including first and second camera feeds, each feed corresponding to a camera located in a store. The processor stores a video segment in memory, assigned to a person and capturing a portion of a shopping session. The video segment includes first and second camera feed segments, each segment corresponding to a recording of the corresponding camera feed from a starting to an ending timestamp. Playback of the first and second camera feed segments is synchronized, and a slider bar controls a playback progress of the camera feed segments. The processor displays the camera feed segments and copies of the slider bar on the display. The processor receives an instruction from at least one of the copies of the slider bar to adjust the playback progress of the camera feed segments and adjusts the playback progress.

Abstract: An access control system that includes an access control device configured to receive transaction information that identifies a member identifier for a member and a purchased item. The access control device is configured to compare the purchased item to items in an item list and to determine whether the purchased item matches any items in the item list. The access control device is configured to store an authorization for the member identifier to access an augmented reality experience in response to a match. The access control device is configured to receive a number of session points earned by the member from interacting with the augmented reality experience, to add the received number of session points to the cumulative number of session points for the member, to identify a reward based on the cumulative session points, and to send reward information for retrieving the identified reward to the member.

Abstract: An object tracking system includes a 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 receive the frame, to detect an object within a zone of the frame, and to determine a pixel location for the object. The tracking system is further configured to identify a zone and a shelf of the rack based on the pixel location, to identify an item based on the identified zone and the identified shelf of the rack, and to add the identified item to a digital cart associated with a person.

Abstract: An object tracking system includes a sensor and a tracking system. The sensor is configured to capture a first frame of a global plane for at least a portion of a space. The tracking system is configured to receive a first coordinate in the global plane where a first marker is located in the space and to receive a second coordinate in the global plane where a second marker is located in the space. The tracking system is further configured to identify the first marker and the second marker within the first frame, to determine a first pixel location in the first frame for the first marker, to determine a second pixel location in the first frame for the second marker, and to generate a homography based on the first coordinate, the second coordinate, the first pixel location, and the second pixel location.

Abstract: An image sensor is positioned such that a field-of-view of the image sensor encompasses at least a portion of a rack storing items. The image sensor generates angled-view images of the items stored on the rack. A tracking subsystem determines that a person has interacted with the rack and receives image frames of the angled-view images. The tracking subsystem determines that the person interacted with a first item stored on the rack. A first image is identified associated with a first time before the person interacted with the first item, and a second image is identified associated with a second time after the person interacted with the first item. If it is determined, based on a comparison of the first and second images, that the item was removed from the rack, the first item is assigned to the person.

Abstract: 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.

Abstract: 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, in a first region of the display, a virtual layout of a virtual store, configured to emulate a physical layout of the physical store. The virtual layout includes virtual racks assigned to physical racks. The virtual layout emulates the physical layout of the physical store. The processor receives an indication of an event associated with the first physical rack and accordingly displays the 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.

Abstract: 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, second, and third 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. The updated second candidate list includes a probability that the second object is associated with the first identifier and a probability that the second object is associated with the second identifier.

Abstract: A validation system that includes a server and a validation terminal. The server is configured to receive location information for a mobile device and to determine the mobile device is at a registered location. The server is further configured to process a transaction for the products in a digital cart, to generate a barcode, and to encrypt the barcode using a private key linked with the registered location. The server is further configured to send the encrypted barcode to the mobile device. The validation engine is configured to scan the encrypted barcode using a barcode reader and to decrypt the encrypted barcode using the stored public key that is linked with the private key. The validation terminal is further configured to indicate the transaction is valid in response to decrypting the encrypted barcode using the public key.

Abstract: A system includes a sensor, a weight sensor, and a tracking subsystem. The tracking subsystem receives an image feed of top-view images generated by the sensor and weight measurements from the weight sensor. The tracking subsystem detects an event associated with an item being removed from a rack in which the weight sensor is installed. The tracking subsystem determines that a first and second person may be associated with the event. After the item exits the rack, the subsystem tracks the item and calculates a velocity of the item as it is moved through the space. The subsystem identifies, based on the calculated velocity, a frame in which the velocity of the item is less than a threshold velocity. The subsystem determines whether the first or second person is nearer the item in the identified frame. If the first person is nearer, the item is assigned to the first person.

Abstract: 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.

Abstract: 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.

Abstract: A tracking system includes a set of cameras, a kiosk, and a tracking server. The kiosk receives a payment amount from a person. The tracking server extracts features of the person from an image feed received from the set of cameras. The tracking server generates a session identifier that is associated with the payment amount and a unique code. The unique code represents at least one of the payment amount and features of the person. The tracking server sends a message to the kiosk to provide a ticket corresponding to the payment amount and the unique code to the person. The tracking server receives a digital cart associated with the person comprising items and a total cash value of the items. The tracking server concludes a transaction by deducting the total cash value from the payment amount.

Abstract: 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.