Blank Rome LLP
Fast histogram-based object tracking
Systems, methods, and computer readable media to rapidly identify and track an arbitrary sized object through a temporal sequence of frames is described. The object being tracked may initially be identified via a specified or otherwise known region-of-interest (ROI). A portion of that ROI can be used to generate an initial or reference histogram and luminosity measure, metrics that may be used to identify the ROI in a subsequent frame. For a frame subsequent to the initial or reference frame, a series of putative ROIs (each having its own location and size) may be identified and the “best” of the identified ROIs selected. As used here, the term “best” simply means that the more similar two frames' histograms and luminosity measures are, the better one is with respect to the other.
Apple Inc.
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Benjamin M. Jianping Geoffrey T. Xiaoxing
Olson Zhou Anneheim Li