Abstract
We present in this paper a framework for articulated hand pose estimation and evaluation. Within this framework we implemented recently published methods for hand segmentation and inference of hand postures. We further propose a new approach for the segmentation and extend existing convolutional network based inference methods. Additionally, we created a new dataset that consists of a synthetically generated training set and accurately annotated test sequences captured with two different consumer depth cameras. The evaluation shows that we can improve with our methods the state-of-the-art. To foster further research, we will make all sources and the complete dataset used in this work publicly available.
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Riegler, G., Ferstl, D., Rüther, M., Bischof, H. (2015). A Framework for Articulated Hand Pose Estimation and Evaluation. In: Paulsen, R., Pedersen, K. (eds) Image Analysis. SCIA 2015. Lecture Notes in Computer Science(), vol 9127. Springer, Cham. https://doi.org/10.1007/978-3-319-19665-7_4
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DOI: https://doi.org/10.1007/978-3-319-19665-7_4
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