Abstract: 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.

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: 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: 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: A system includes a first sensor and a sensor client. During an initial time interval, the sensor client receives top-view images generated by the first sensor and detects contours in the images. The sensor client determines, based on the contours, regions of the top-view images generated by the first sensor to exclude during object tracking. During a subsequent time interval, the sensor client receives a second top-view image generated by the first sensor and detects a contour in the image. The sensor client determines pixel coordinates of the second contour and determines whether at least a threshold percentage of the second pixel coordinates overlap with the region to exclude during object tracking. If at least the threshold percentage of the second pixel coordinates overlap with the region to exclude, a position for tracking the second contour is not determined.

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: 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: 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 or a second person may be associated with the event. In response to determining that the first or second person may be associated with the event, buffer frames are stored of top-view images generated by the sensor during a time period associated with the event. The tracking subsystem then determines, using at least one of the stored buffer frames and a first action-detection algorithm, whether an action associated with the event was performed by the first person or the second person.

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: 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. The first frame includes a region-of-interest (ROI) marker within the space. The system is further configured to identify pixel locations within the first frame corresponding with the ROI marker and to define a zone for subsequent frames from the sensor corresponding with the pixel locations. The system is further configured to receive a second frame from the sensor, to detect an object within the zone, and to identify the object. The system is further configured to determine a person is within the second frame and to modify a digital cart that is associated with the person based on the identified object.

Abstract: An object tracking system that includes a sensor, a weight sensor, and a tracking system. The sensor 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 identify a marker on an item within a predefined zone in the frame, and to identify the item associated with the identified marker. The tracking system is further configured to determine a pixel location for a person, to determine the person is within the predefined zone associated with the, and to add the identified item to a digital cart associated with the person.

Abstract: 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.

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 receives image frames of the angled-view images. The tracking subsystem detects that a trigger event has occurred. A set of one or more image frames from the image feed are determined that are associated with the detected trigger event. A region-of-interest of the image frame is determined based on the pixel position of the wrist of the person. The region-of-interest includes a subset of the pixels of the image frame. A first item in the determined region-of-interest using an object detection algorithm. The identified first item is assigned to the person.

Abstract: An object tracking system includes a first sensor, a second sensor, and a tracking system. The tracking system is configured to determine that the first current pixel location for the shelf marker does not match a first expected pixel location for the shelf marker. The tracking system is further configured to determine a second current pixel location for the shelf marker within a second frame from the second sensor, to recalibrate the first sensor when the second current pixel location for the shelf marker matches the second pixel location for the shelf marker and to update the first pixel location with the first current pixel location and the second pixel location with the second current pixel location when the second current pixel location for the shelf marker does not match the second pixel location for the shelf marker.

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: 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.

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 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.

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 or a second person may be associated with the event. In response to determining that the first or second person may be associated with the event, buffer frames are stored of top-view images generated by the sensor during a time period associated with the event. The tracking subsystem then determines, using at least one of the stored buffer frames and a first action-detection algorithm, whether an action associated with the event was performed by the first person or the second person.

Abstract: 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.