Abstract: A method of generating neural network training image annotations includes training a first neural network to identify and segment hands in images using a first set of 2D images with hand portions segmented in each image; substantially simultaneously capturing both a second set of 2D images, and a third set of images including depth images, depicting hands holding a particular type of object; correlating each of the second set of images with corresponding images of the third set to identify and segment foregrounds from backgrounds in the second set of images; applying the trained first neural network to the identified foregrounds to identify hand portions of the foregrounds and segment object portions from identified hand portions; and training a second neural network, using the segmented object portions of the second set of images as training data, to identify the particular type of object in new images.

Abstract: Methods for accurate object tracking are disclosed herein. An example the method includes receiving, from a first optical imaging assembly having a first field of view (FOV), a first image captured over the first FOV and based on a decode of an indicia associated with an object of interest, identifying the object of interest within the first image. The method further includes determining a location of the object of interest within the first image and mapping the location of the object of interest within the first image to a predicted location of the object of interest within a second image, the second image being received from a second optical imaging assembly having a second FOV and the second image being captured over the second FOV.

Abstract: A method of generating neural network training image annotations includes training a first neural network to identify and segment hands in images using a first set of 2D images with hand portions segmented in each image; substantially simultaneously capturing both a second set of 2D images, and a third set of images including depth images, depicting hands holding a particular type of object; correlating each of the second set of images with corresponding images of the third set to identify and segment foregrounds from backgrounds in the second set of images; applying the trained first neural network to the identified foregrounds to identify hand portions of the foregrounds and segment object portions from identified hand portions; and training a second neural network, using the segmented object portions of the second set of images as training data, to identify the particular type of object in new images.

Abstract: Methods for accurate object tracking are disclosed herein. An example the method includes receiving, from a first optical imaging assembly having a first field of view (FOV), a first image captured over the first FOV and based on a decode of an indicia associated with an object of interest, identifying the object of interest within the first image. The method further includes determining a location of the object of interest within the first image and mapping the location of the object of interest within the first image to a predicted location of the object of interest within a second image, the second image being received from a second optical imaging assembly having a second FOV and the second image being captured over the second FOV.

Abstract: Systems and methods for synchronizing a barcode decode with a video camera to improve accuracy of retail POS loss prevention are disclosed herein. An example system includes a video camera configured to capture a set of frames associated with a target object, the set of frames having a first number of frames; a symbology reader configured to: (i) decode a symbology attached to the target object and (ii) generate a signal based on the successful decoding of the symbology attached to the target object; and a processor configured to analyze a sub-set of the set of frames based on the signal, the sub-set of the set of frames having a second number of frames that is smaller than the first number of frames.