Abstract: An accessory mounting apparatus and system are provided. The system includes a structural framing, an accessory mounting apparatus, and an accessory. The accessory mounting apparatus includes a first component, a second component, and a connector arm. The first component includes a first base, a first rail connector that extends from the first base, and one or more first connectors. The second component includes a second base and a second rail connector that extends from the second base. The connector arm includes a second connector and a mounting arm, and the second connector is coupled to the one or more first connectors. Further, the first rail connector and the second rail connector are configured to engage a first rail slot and a second rail slot of the structural framing, accordingly. Additionally, the accessory is coupled to the mounting arm.

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 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 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.

Abstract: A system includes a first sensor and a sensor client. During an initial time interval, the sensor client receives images generated by the first sensor and detects contours in the images. The sensor client determines, based on the contours, regions of the images generated by the first sensor to exclude during object tracking. During a subsequent time interval, the sensor client receives a second 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 accessory mounting apparatus and system are provided. The system includes a structural framing, an accessory mounting apparatus, and an accessory. The accessory mounting apparatus includes a first component, a second component, and a connector arm. The first component includes a first base, a first rail connector that extends from the first base, and one or more first connectors. The second component includes a second base and a second rail connector that extends from the second base. The connector arm includes a second connector and a mounting arm, and the second connector is coupled to the one or more first connectors. Further, the first rail connector and the second rail connector are configured to engage a first rail slot and a second rail slot of the structural framing, accordingly. Additionally, the accessory is coupled to the mounting arm.

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 system includes a sensor and a tracking subsystem. The tracking subsystem receives an image feed of top-view images generated by the sensor and detects an event associated with a portion of a person entering a zone adjacent to a rack. The tracking subsystem determines that a first and second person may be associated with the event. 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: 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 physical space 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 of the physical structure based on the pixel location, to identify an item based on the identified zone.

Abstract: A device configured to receive a rack identifier for a rack that is configured to hold items. The device is further configured to identify a master template that is associated with the rack. The device is further configured to receive images of the plurality of items on the rack and to combine the images into a composite image of the rack. The device is further configured to identify shelves on the rack within the composite image and to generate bounding boxes that correspond with an item on the rack. The device is further configured to associate each bounding box with an item identifier and an item location. The device is further configured to generate a rack analysis message based on a comparison of the item locations for each bounding box and the rack positions from the master template and to output the rack analysis message.

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 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 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.

Abstract: 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.

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 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.

Abstract: A device configured to detect a triggering event at a platform and to capture a depth image of items on the platform using a three-dimensional (3D) sensor. The device is further configured to determine an object pose for each item on the platform and to identify one or more cameras from among a plurality of cameras based on the object pose for each item on the platform. The device is further configured to capture one or more images of the items on the platform using the identified cameras and to identify items within the one or more images based on features of the items. The device is further configured to identify a user associated with the identified items on the platform, to identify an account that is associated with the user, and to associate the identified items with the account of the user.

Abstract: A device configured to capture a first image of an item on a platform using a camera and to determine a first number of pixels in the first image that corresponds with the item. The device is further configured to capture a first depth image of an item on the platform using a three-dimensional (3D) sensor and to determine a second number of pixels within the first depth image that corresponds with the item. The device is further configured to determine that the difference between the first number of pixels in the first image and the second number of pixels in the first depth image is less than the difference threshold value, to extract the plurality of pixels corresponding with the item in the first image from the first image to generate a second image, and to output the second image.

Abstract: A system for updating a training dataset of an item identification model determines that an item is not included in a training dataset. In response to determining that the item is not included in the training dataset, the system obtains an identifier of the item. The system detects a triggering event at a platform, where the triggering event corresponds to a user placing the item on a platform. The system captures images of the item. The system extracts a set of features associated with the item from the images. The system associates the item to the identifier and the set of features. The system adds a new entry to the training dataset, where the new entry represents the item labeled with the identifier and the set of features.

Abstract: A system for identifying items based on aggregated metadata obtains images of an item. The system extracts a set of features from images of the item. The system identifies a first value of a first feature associated with a first image of the item. The system identifies a second value of the first feature associated with a second image of the item. The system aggregates the first value and the second value. The system associates the item to the aggregated first value and the second value, where the aggregated first value and the second value represent the first feature of the item. The system adds a new entry for each image of the item to a training dataset associated with an item identification model.