A method for implementation by one or more data processors forming part of at least one computing system, the method comprising: receiving combined color and depth data for a field of view; defining at least one proposed bounding polygon for the field of view by: bifurcating the combined color and depth data into color information and depth information; applying depth segmentation to the depth data to identify one or more objects within the depth information to result in at least one bounding polygon; and applying the at least one bounding polygon to the color data to form a color image and/or a combined color and depth image; further processing the color image and/or the combined color and depth image by: determining, using a binary classifier having at least one machine learning model trained using a plurality of images of known objects, whether each proposed bounding polygon encapsulates an object; providing the image data within each bounding polygon that is determined to encapsulate an object to a first object classifier having at least one machine learning model trained using a plurality of images of known objects, to classify the object encapsulated within the respective bounding polygon; providing the image data within each bounding polygon that is determined to encapsulate an object to a second object classifier having at least one machine learning model trained using a plurality of images of known objects, to classify the object encapsulated within the respective bounding polygon; determining a final classification for each bounding polygon based on the output of the first object classifier and the output of the second object classifier; and providing data characterizing the final classification for each bounding polygon. receiving combined color and depth data for a field of view; defining at least one proposed bounding polygon for the field of view by: bifurcating the combined color and depth data into color information and depth information; applying depth segmentation to the depth data to identify one or more objects within the depth information to result in at least one bounding polygon; and applying the at least one bounding polygon to the color data to form a color image and/or a combined color and depth image; bifurcating the combined color and depth data into color information and depth information; applying depth segmentation to the depth data to identify one or more objects within the depth information to result in at least one bounding polygon; and applying the at least one bounding polygon to the color data to form a color image and/or a combined color and depth image; further processing the color image and/or the combined color and depth image by: determining, using a binary classifier having at least one machine learning model trained using a plurality of images of known objects, whether each proposed bounding polygon encapsulates an object; providing the image data within each bounding polygon that is determined to encapsulate an object to a first object classifier having at least one machine learning model trained using a plurality of images of known objects, to classify the object encapsulated within the respective bounding polygon; providing the image data within each bounding polygon that is determined to encapsulate an object to a second object classifier having at least one machine learning model trained using a plurality of images of known objects, to classify the object encapsulated within the respective bounding polygon; determining a final classification for each bounding polygon based on the output of the first object classifier and the output of the second object classifier; and providing data characterizing the final classification for each bounding polygon. determining, using a binary classifier having at least one machine learning model trained using a plurality of images of known objects, whether each proposed bounding polygon encapsulates an object; providing the image data within each bounding polygon that is determined to encapsulate an object to a first object classifier having at least one machine learning model trained using a plurality of images of known objects, to classify the object encapsulated within the respective bounding polygon; providing the image data within each bounding polygon that is determined to encapsulate an object to a second object classifier having at least one machine learning model trained using a plurality of images of known objects, to classify the object encapsulated within the respective bounding polygon; determining a final classification for each bounding polygon based on the output of the first object classifier and the output of the second object classifier; and providing data characterizing the final classification for each bounding polygon.