Abstract
We present an approach for video based human motion capture using a static multi camera setup. The image data of calibrated video cameras is used to generate dense volumetric reconstructions of a person within the capture volume. The 3d reconstructions are then used to fit a 3d cone model into the data utilizing the Iterative Closest Point (ICP) algorithm. We can show that it is beneficial to use multi camera data instead of a single time of flight camera to gain more robust results in the overall tracking approach.
This work was supported by a grant from the Ministry of Science, Research and the Arts of Baden-Württemberg.
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Feldmann, T., Mihailidis, I., Schulz, S., Paulus, D., Wörner, A. (2010). Online Full Body Human Motion Tracking Based on Dense Volumetric 3D Reconstructions from Multi Camera Setups. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_8
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DOI: https://doi.org/10.1007/978-3-642-16111-7_8
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