Abstract
In this work we propose an online approach to compute a more precise assignment between parts of an upper human body model to RGBD image data. For this, a Self-Organizing Map (SOM) will be computed using a set of features where each feature is weighted by a relevance factor (RFSOM). These factors are computed using the generalized matrix learning vector quantization (GMLVQ) and allow to scale the input dimensions according to their relevance. With this scaling it is possible to distinguish between the different body parts of the upper body model. This method leads to a more precise positioning of the SOM in the 2.5D point cloud, a more stable behavior of the single neurons in their specific body region, and hence, to a more reliable pose model for further computation. The algorithm was evaluated on different data sets and compared to a Self-Organizing Map trained with the spatial dimensions only using the same data sets.
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References
Haker, M., Böhme, M., Martinetz, T., Barth, E.: Self-Organizing Maps for Pose Estimation with a Time-of-Flight Camera. In: Kolb, A., Koch, R. (eds.) Dyn3D 2009. LNCS, vol. 5742, pp. 142–153. Springer, Heidelberg (2009)
Shotton, J., Fitzgibbon, A.W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-Time Human Pose Recognition in Parts from Single Depth Images. In: IEEE CVPR 2011, pp. 1297–1304 (2011)
Schneider, P., Biehl, M., Hammer, B.: Adaptive relevance matrices in learning vector quantization. Neural Computation 21, 3532–3561 (2009)
Otsu, N.: A Threshold Selection Method from Gray-level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)
Viola, P., Jones, M.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005, vol. 1, pp. 886–893 (2005)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on PAMI 24(7), 971–987 (2002)
Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics SMC-3(6), 610–621 (1973)
Klingner, M., Hellbach, S., Kästner, M., Villmann, T., Böhme, H.J.: Modeling Human Movements with Self-Organizing Maps using Adaptive Metrics. In: NC2 2012, pp. 14–19 (2012)
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Klingner, M., Hellbach, S., Riedel, M., Kaden, M., Villmann, T., Böhme, HJ. (2014). RFSOM – Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_15
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DOI: https://doi.org/10.1007/978-3-319-07695-9_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07694-2
Online ISBN: 978-3-319-07695-9
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