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학술대회 Adaptive Directional Walks For Pose Estimation From Single Body Depths
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김재환, 이준석
International Conference on Multimedia and Expo (ICME) 2020, pp.1-6
In this paper, we introduce a novel body pose estimation method based on single depth images with our proposed random forest classifier, whereby it is possible to estimate the positions of joints directly with significant accuracy. We train randomized classification trees based on ajoint entropy objective function combined with the geodesic distances and directional vectors simultaneously, to estimate the probability distribution for the label of the directional vector towards a particular bodyjoint by considering the geodesic structural information. At a test step, an arbitrary point moves as much as the magnitude of the probability predicted adaptively by following the predefined kinematic graph, which is referred to as adaptive directional walks. Numerical and visual experiments with real datasets confirm the usefulness of the proposed method.
KSP 제안 키워드
Classification tree, Estimation method, Geodesic distance, Pose estimation, Probability distribution, Structural information, Visual experiments, objective function, random forest classifier, single depth images