Abstract
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.
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© 2003 Springer-Verlag Berlin Heidelberg
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Xiong, T., Debrunner, C. (2003). Monte Carlo Visual Tracking Using Color Histograms and a Spatially Weighted Oriented Hausdorff Measure. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_24
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DOI: https://doi.org/10.1007/978-3-540-45179-2_24
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