We propose a novel, model-based approach for articulated object detection and pose estimation that does not need any low-level feature extraction or foreground segmentation and thus eliminates this error-prone step. Our approach works directly on the input color image and is based on a new kind of divergence of the color distribution between an object hypothesis and its background. Consequently, we get a color distribution of the target object for free. We further propose a coarse-to-fine and hierarchical algorithm for fast object localization and pose estimation. Our approach works significantly better than segmentation-based approaches in cases where the segmentation is noisy or fails, e.g. scenes with skin-colored backgrounds or bad illumination that distorts the skin color. We also present results by applying our novel approach to markerless hand tracking. © 2011 Springer-Verlag.
CITATION STYLE
Mohr, D., & Zachmann, G. (2011). Segmentation-free, area-based articulated object tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6938 LNCS, pp. 112–123). https://doi.org/10.1007/978-3-642-24028-7_11
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