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Discriminative Appearance Models for Pictorial Structures

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons71836

Andriluka,  Mykhaylo
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Zitation

Andriluka, M., Roth, S., & Schiele, B. (2012). Discriminative Appearance Models for Pictorial Structures. International Journal of Computer Vision, 99(3), 259-280. doi:10.1007/s11263-011-0498-z.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-F706-9
Zusammenfassung
In this paper we consider people detection and articulated pose estimation, two closely related and challenging problems in computer vision. Conceptually, both of these problems can be addressed within the pictorial structures framework (Felzenszwalb and Huttenlocher in Int. J. Comput. Vis. 61(1):55–79, 2005; Fischler and Elschlager in IEEE Trans. Comput. C-22(1):67–92, 1973), even though previous approaches have not shown such generality. A principal difficulty for such a general approach is to model the appearance of body parts. The model has to be discriminative enough to enable reliable detection in cluttered scenes and general enough to capture highly variable appearance. Therefore, as the first important component of our approach, we propose a discriminative appearance model based on densely sampled local descriptors and AdaBoost classifiers. Secondly, we interpret the normalized margin of each classifier as likelihood in a generative model and compute marginal posteriors for each part using belief propagation. Thirdly, non-Gaussian relationships between parts are represented as Gaussians in the coordinate system of the joint between the parts. Additionally, in order to cope with shortcomings of tree-based pictorial structures models, we augment our model with additional repulsive factors in order to discourage overcounting of image evidence. We demonstrate that the combination of these components within the pictorial structures framework results in a generic model that yields state-of-the-art performance for several datasets on a variety of tasks: people detection, upper body pose estimation, and full body pose estimation.