N. Navab

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Publications (2)0 Total impact

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    Conference Proceeding: Estimating human 3D pose from Time-of-Flight images based on geodesic distances and optical flow
    L.A. Schwarz, A. Mkhitaryan, D. Mateus, N. Navab
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    ABSTRACT: In this paper, we present a method for human full-body pose estimation from Time-of-Flight (ToF) camera images. Our approach consists of robustly detecting anatomical landmarks in the 3D data and fitting a skeleton body model using constrained inverse kinematics. Instead of relying on appearance-based features for interest point detection that can vary strongly with illumination and pose changes, we build upon a graph-based representation of the ToF depth data that allows us to measure geodesic distances between body parts. As these distances do not change with body movement, we are able to localize anatomical landmarks independent of pose. For differentiation of body parts that occlude each other, we employ motion information, obtained from the optical flow between subsequent ToF intensity images. We provide a qualitative and quantitative evaluation of our pose tracking method on ToF sequences containing movements of varying complexity.
    Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on; 04/2011
  • Source
    Conference Proceeding: Tracking planes with Time of Flight cameras and J-linkage
    L.A. Schwarz, D. Mateus, J. Lallemand, N. Navab
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    ABSTRACT: In this paper, we propose a method for detection and tracking of multiple planes in sequences of Time of Flight (ToF) depth images. Our approach extends the recent J-linkage algorithm for estimation of multiple model instances in noisy data to tracking. Instead of randomly selecting plane hypotheses in every image, we propagate plane hypotheses through the sequence of images, resulting in a significant reduction of computational load in every frame. We also introduce a multi-pass scheme that allows detecting and tracking planes of varying spatial extent along with their boundaries. Our qualitative and quantitative evaluation shows that the proposed method can robustly detect planes and consistently track the hypotheses through sequences of ToF images.
    Applications of Computer Vision (WACV), 2011 IEEE Workshop on; 02/2011

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