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A Biologically Motivated Approach to Human Body Pose Tracking in Clutter

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83902

Engel,  D
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83871

Curio,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Engel, D., & Curio, C. (2007). A Biologically Motivated Approach to Human Body Pose Tracking in Clutter. Poster presented at 10th Tübinger Wahrnehmungskonferenz (TWK 2007), Tübingen, Germany.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CCCD-8
Abstract
In this study we present a biologically motivated learning-based computer vision approach to human pose estimation and tracking in clutter. The approach consists of two interconnected modules: human posture estimation from monocular images and tracking a person’s location in video footage. Full body pose estimation is approached with methods from statistical learning theory: A mapping from biologically plausible complex features (similar to [1]) into a pose space is learned using kernel based techniques (i.e. Support Vector Machines and kernel ridge regression). The pose space is derived from a human body model based on 3D joint positions. To tackle the ambiguities inherent to the projection of a 3D scene onto a monocular image our approach employs a one-to-many mapping scheme which maps, in a mixture-of-experts fashion [2], to several possible 3D poses. A key feature of the presented framework is the feedback matching pathway which evaluates the likelihood of a generated hypothesis in an intermediate feature space based on a robust medial axis transformation. The approach of [3] is hereby extended to clutter. The fusion of bottom-up and top-down techniques exploits the advantages of both approaches by being able to generate multiple hypotheses fast in a feedforward manner without losing the ability to evaluate the hypotheses in the original image space. Tracking is investigated as the problem of finding a bounding box of a person throughout a video sequence taking into account possible shape deformations. Based on the ability to track a person a temporal filtering framework with constraints of natural movement is employed to further disambiguate several hypotheses and to arrive at a stable and robust pose estimate. To generate the needed amount of training images with corresponding ground-truth pose information we use realistic computer graphics models driven by motion capture data embedded into clutter by alpha-blending. Overall, we explore the robustness of our framework against background changes and its generalization capabilities to novel actors, actions and real world imagery.