Abstract: Apparatus having model-based machine learning and inferencing logic for controlling object transfer, comprises: an image input component to receive image data derived from a captured image of an object; a captured image classifier to 5 generate a first classification of the object by activating a trained model to analyse the image data; an input component to receive an object identifier for the object; an object identification classifier to generate a second classification of the object according to the object identifier; matching logic to detect failure to reconcile the first and second classification; heuristic logic responsive to the matching logic to 10 determine a causal factor in the failure; and training logic, operable when the heuristic logic determines that a causal factor in the failure to reconcile is a deficient first classification, to provide model training input comprising the image data and the object identifier to the model-based machine learning logic.

Abstract: Provided is machine learning apparatus comprising: a dataset for input to a training procedure of a machine learning model; data capture logic operable to capture from an object at least one datum for inclusion in the dataset; association logic operable to derive an additional characteristic of the object; annotator logic operable in response to the data capture logic and the association logic to create an annotation linking the additional characteristic with the at least one datum; storage logic operable to store the or each datum with an associated annotation in the dataset; and input logic to supply the dataset as machine learning input.

Abstract: A computer-implemented method comprises obtaining first data comprising a first collection of coordinate data representing a position, or positions, of a first group of one or more objects detected in a first frame of a scene and obtaining second data comprising a second collection of coordinate data representing a position, or positions, of a second group of one or more objects detected in a second frame of the scene, wherein the second frame represents a different view of the scene than the first frame. The method further comprises determining whether any of the first group of objects correspond to any of the second group of objects in the scene based on the first collection of coordinate data and the second collection of coordinate data, wherein the first frame and the second frame are obtained from image data captured from a single camera position.

Abstract: System, apparatus and method of image processing to detect a substance spill on a solid surface such as a floor is disclosed. First data representing a first image, captured by an image sensor, of a region including a solid surface, is received. A trained semantic segmentation neural network is applied to the first image data to determine, for each pixel of the first image, a spill classification value associated with the pixel, the determined spill classification value for a given pixel indicating the extent to which the trained semantic segmentation neural network estimates, based on its training, that the given pixel illustrates a substance spill. The presence of a substance spill on the solid surface is detected based on the determined spill classification values of the pixels of the first image.

Abstract: A computer-implemented method comprises obtaining first data comprising a first collection of coordinate data representing a position, or positions, of a first group of one or more objects detected in a first frame of a scene and obtaining second data comprising a second collection of coordinate data representing a position, or positions, of a second group of one or more objects detected in a second frame of the scene, wherein the second frame represents a different view of the scene than the first frame. The method further comprises determining whether any of the first group of objects correspond to any of the second group of objects in the scene based on the first collection of coordinate data and the second collection of coordinate data, wherein the first frame and the second frame are obtained from image data captured from a single camera position.