Monte Carlo Visual Tracking Using Color Histograms and a Spatially Weighted Oriented Hausdorff Measure

Color-based and edge-based trackers based on sequential Monte Carlo filters have been shown to be robust and versatile for a modest computational cost. However, background features with characteristics similar to the tracked object can distract them. Robustness can be further improved through the integration of multiple features such that a failure in one feature will not cause the tracker to fail. We present a new method of integrating a shape and a color feature such that even if only a single feature provides correct results, the feature tracker can track correctly. We also introduce a new Hausdorff-based shape similarity metric that we call the spatially weighted oriented Hausdorff similarity measure (SWOHSM). The approach is shown to be robust on both face tracking and automobile tracking applications.