Abstract: A camera with a field of view of a shelf may acquire images of items on the shelf at subsequent times. An analysis of each image yields a set of estimated locations of the items on the shelf. Based on a working volume of the shelf, a subset of valid estimated locations of the items at the shelf may be determined. Thereafter, a count of the items on the shelf at a particular time may be determined using the subset of valid estimated item locations. By comparing the count of items at the shelf at two subsequent times, a change in quantity of the items on the shelf may be determined. Interaction data may be generated in response to detecting the change in quantity of items at the shelf.

Abstract: A data processing pipeline controller receives a request, from a data iterator associated with a machine learning model, for a data output of a module in the data processing pipeline, wherein each module in the data processing pipeline has an associated cache. The controller determines whether a data output of the module is stored in the associated cache and responsive to the data output being stored in the associated cache, provides the data output from the associated cache to the data iterator without processing data through the module.

Abstract: Image data representative of an inventory location may be acquired by cameras. Described are techniques to process such image data to remove one or more perspective effects. For example, transformation data that corresponds to the inventory location may first be determined. Such transformation data is determined based on associations between points in a common plane and alternate points in a virtual camera plane. Transformed image data may then be generated by applying the transformation data to the image data that is representative of the inventory location.

Abstract: Images of a fixture may be acquired by cameras positioned with a field-of-view of the fixture. Such images are processed to identify estimated tops of items at the fixture. Using the estimated tops and item data for items designated for stowage at the fixture, one or more estimated locations of items (such as bounding boxes representative of the items) may be determined. Each estimated location for an item is tested for validity. For example, each estimated location is checked to see if the estimated location is within a working volume of the fixture. If the estimated location of the item is within the working volume, the item is determined to be valid. Otherwise, the item is deemed invalid.

Abstract: Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.

Abstract: Images of a fixture such as a shelf holding items may be acquired by cameras and processed to count the quantity of items at the fixture. A top of an item is determined in the image. Given information about the items designated for stowage at the fixture and the location of the top, a three-dimensional (3D) bounding box indicative of a volume is determined relative to the fixture. Bounding boxes which extend outside the boundaries of the fixture are disregarded. Remaining bounding boxes may then be analyzed to determine a measured height of the item(s) in a stack. The measured height may be divided by a per-item height to determine a quantity of items in the stack. The quantities in multiple stacks may be summed to determine a quantity at the fixture.

Abstract: Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.

Abstract: Images may be acquired by cameras and processed to detect the presence of objects. Described are techniques to use rectified images for further processing such as object detection, object identification, and so forth. In one implementation, an image from a camera is processed to produce a rectified image having an apparent perspective from overhead. For example, the rectified image may be an image that appears to have been obtained from a virtual camera that is above the shelf and having a field-of-view that looks downward. The rectified image may be an orthonormal projection of pixels in the acquired image, relative to a plane of the surface upon which the objects are resting.