Subpixel Object Tracking in RGB Intensity and Depth Imagery

This paper examines the challenges of object tracking algorithms performing on RGB-D data. We analyze and quantify the performance of common state-of-the-art tracking methods performing on the intensity and depth channels. This paper investigates the tracking performance characteristics of intensity and depth channel processing separate and in conjunction within complex RGB-D scenes with moving objects. A new assessment metric is introduced, called template dissimilarity assessment (TDA), to score the performance of individual tracking methods and determine when track is lost and re-initialization is appropriate. Various tracking metrics are directly compared between intensity and depth data sets. The overall performance and the advantages of the intensity and depth tracking approaches are emphasized. Lastly, the overall performance assessment includes the algorithmic computational expense, measured via processor timing tests.