Zebra Technologies Corporation

Vincent J Daempfle

Christopher J Fjellstad

Sajan Wilfred

Duanfeng He

Abhilash Gururaja

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.

Method to generate neural network training image annotations

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.

Method for accurate object tracking with color camera in multi planar scanners

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.

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Plainview, NY, United States of America

Vincent J Daempfle