Markov-based Silhouette Extraction for Three--Dimensional Body Tracking in Presence of Cluttered Background

We propose a novel method to detect human body contours in presence of clutter and complex texture. Contours are extracted using a novel Markovbased approach which learns a texture along a given scanline in order to detect texture crossings. In contrast to conventional silhouette detection algorithms based on gradient, our texture boundary detection method allows extraction of silhouettes of textured and non-textured objects under difficult conditions such as having a cluttered/moving background. We demonstrate on demanding examples of monocular body tracking that our proposed method yields better results than gradient-based techniques.

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