Abstract: A system for refining an item identification model 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 from at least one of the images. The system identifies the item based on the set of features. The system receives an indication that the item is not identified correctly. The system receives an identifier of the item. The system identifies the item based on the identifier of the item. The system feeds the identifier of the item and the images to the item identification model. The system retrains the item identification model to learn to associate the item to the images. The system updates the set of features based on the determined association between the item and the images.

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 an object. A pixel position of a body part of a person is determined in at least a subset of the received image frames, thereby determining a set of pixel positions of the body part. An aggregated body part position is determined based on the set of pixel positions. If the aggregated body part position is determined to correspond to a position associated with the object, a trigger signal is provided indicating an interaction event has occurred.

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 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: In response to detecting a first triggering event corresponding to placement of a first item on a platform, a first image of the platform is captured. A first item identifier of the first item is identified and stored in a memory. In response to detecting a second triggering event corresponding to placement of a second item on the platform, a second image of the platform is captured. The second image is compared with the first image. Upon determining that the first item depicted in the second image overlaps with the first item depicted in the first image and the overlap equals or exceeds a threshold, the first item identifier is assigned to the first item depicted in the second image. A second item identifier of the second item is identified, and information associated with the first item identifier and the second item identifier is displayed on a user interface device.

Abstract: In response to detecting a triggering event corresponding to placement of a first item on a platform, a plurality of images of the first item are captured. For each image of the first item, a cropped image is generated including a bounding box around the first item depicted in the image. For each cropped image, a ratio is calculated between a portion of a total area within the bounding box occupied by the first item and the total area. If the ratio equals or exceeds a minimum threshold, an item identifier associated with the first item is identified based on the cropped image. On the other hand, if the ratio is below the threshold, the cropped image is discarded. A particular item identifier is selected from a set of cropped images that were not discarded.

Abstract: A device captures an image of a first item and generates a first encoded vector for the image. The device identifies a set of items that have at least one attribute in common with the first item. The device determines the identity of the first item based at least on attributes of the first item. The device determines that a confidence score associated with the identity of the first item is less than a threshold percentage. In response, the device determines a height of the first item. The device identifies item(s) with average heights within a threshold range from the height of the first item. The device compares the first encoded vector with a second encoded vector associated with a second item from the identified item(s). If the first encoded vector corresponds to the second encoded vector, the device determines that the first item corresponds to the second item.

Abstract: In response to detecting a triggering event corresponding to placement of a first item on a platform, a plurality of images are captured of the first item and a plurality of cropped images are generated based on the first images. An item identifier is identified based on each cropped image, wherein each item identifier is associated with a numerical similarity value. Each cropped image is further tagged as a front image or a back image. A particular item identifier identified for a corresponding cropped image tagged as a front image is selected and associated with the first item. An indicator of the particular item identifier is displayed on a user interface device.

Abstract: A device detects a triggering event that corresponds to a placement of an item on a platform. In response, the device captures an image of the item and generates a first encoded vector for the image. The first encoded vector describes one or more attributes of the item. The device determines that the item is associated with a first container category based on the one or more attributes of the item. The device identifies one or more items that have been identified as having placed inside a container associated with the first container category. The device displays a list of item options that comprises the one or more items on a graphical user interface (GUI). The device receives a selection of a first item from along the list of item options and identifies the first item as being inside the container.

Abstract: In response to detecting a triggering event corresponding to placement of a first item on a platform, a plurality of images are captured of the first item and a plurality of cropped images are generated based on the first images. An item identifier is identified based on each cropped image, wherein each item identifier is associated with a numerical similarity value. Item identifiers associated with a highest and next highest similarity values are selected. When a difference between the highest and the next highest similarity values equals or exceeds a threshold, the item identifier associated with the highest similarity value is associated with the first item placed on the platform. An indicator of the item identifier is displayed on a user interface device.

Abstract: A system for capturing images for training an item identification model obtains an identifier of an 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 causes the platform to rotate. The system causes at least one camera to capture an image of the item while the platform is rotating. The system extracts a set of features associated with the item from the image. The system associates the item to the identifier and the set of features. The system adds a new entry to a training dataset of the item identification model, where the new entry represents the item labeled with the identifier and the set of features.

Abstract: In response to detecting a first triggering event corresponding to placement of a first item on a platform, a plurality of first images are captured of the first item. An item identifier associated with the first item is identified based on the first images and assigned to the first item. In response to detecting a second triggering event corresponding to placement of a second item on the platform, a plurality of second images are captured of the second item, a plurality of cropped images are generated based on the second images, and a plurality of item identifiers are determined for the second item based on the cropped images. When a process for selecting a particular item identifier from the plurality of item identifiers fails, a second item identifier is assigned to the second item based on an association between the first item identifier and the second item identifier.

Abstract: In response to detecting a triggering event corresponding to placement of a first item on a platform, a plurality of images are captured of the first item and a plurality of cropped images are generated based on the images. For each cropped image, a first encoded vector is generated and compared to encoded vectors in an encoded vector library that are tagged as a front image. Based on the comparison, a second encoded vector is selected from the encoded vector library that most closely matches with the first encoded vector. An item identifier is identified that is associated with the second encoded vector. A particular item identifier is selected that is identified for a particular cropped image.

Abstract: A system for capturing images for training an item identification model obtains an identifier of an 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 causes the platform to rotate. The system causes at least one camera to capture an image of the item while the platform is rotating. The system extracts a set of features associated with the item from the image. The system associates the item to the identifier and the set of features. The system adds a new entry to a training dataset of the item identification model, where the new entry represents the item labeled with the identifier and the set of features.

Abstract: An item tracking system comprises a plurality of cameras, a memory storing associations between item identifiers of respective items, and a processor configured to capture a plurality of first images of a first item and identify a first item identifier of the first item based on the first images. The processor captures a plurality of second images of a second item, generates cropped image of the second item from each second image, and identifies an item identifier for each cropped image. Based on the associations stored in the memory, the processor determines that an association exists between the first item identifier of the first item and a second item identifier, and assigns the second item identifier to the second item when at least one of the item identifiers corresponding to the cropped images is the second item identifier.

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 is within a threshold distance of the rack and receives image frames of the angled-view images. A pixel position of a wrist of the person is determined in at least a subset of the received image frames, thereby determining a set of pixel positions of the wrist. An aggregated wrist position is determined based on the set of pixel positions. If the aggregated wrist position is determined to correspond to a position on a shelf of the rack, a trigger signal is provided indicating a shelf-interaction event has occurred.

Abstract: A system for capturing images for training an item identification model obtains an identifier of an 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 causes the platform to rotate. The system causes at least one camera to capture an image of the item while the platform is rotating. The system extracts a set of features associated with the item from the image. The system associates the item to the identifier and the set of features. The system adds a new entry to a training dataset of the item identification model, where the new entry represents the item labeled with the identifier and the set of features.

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 images of the items stored on the rack. Over a period of time, a tracking subsystem tracks a pixel position of the wrist of a person interacting with items stored on the rack, receives image frames of the angled-view images. The tracking subsystem determines whether an item was interacted with by a person and, if so, the identified item is assigned to the person.

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 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.