High precision object segmentation and tracking for use in super resolution video reconstruction

Super resolution image reconstruction allows for the enhancement of images in a video sequence that is superior to the original pixel resolution of the imager. Difficulty arises when there are foreground objects that move differently than the background. A common example of this is a car in motion in a video. Given the common occurrence of such situations, super resolution reconstruction becomes non-trivial. One method for dealing with this is to segment out foreground objects and quantify their pixel motion differently. First we estimate local pixel motion using a standard block motion algorithm common to MPEG encoding. This is then combined with the image itself into a five dimensional mean-shift kernel density estimation based image segmentation with mixed motion and color image feature information. This results in a tight segmentation of objects in terms of both motion and visible image features. The next step is to combine segments into a single master object. Statistically common motion and proximity are used to merge segments into master objects. To account for inconsistencies that can arise when tracking objects, we compute statistics over the object and fit it with a generalized linear model. Using the Kullback-Leibler divergence, we have a metric for the goodness of the track for an object between frames.