Abstract
This paper deals with the problem of labeling a cloud of points as a classification problem, proposing an ensemble of weak classifiers. First, we define a set of geometrical features over small subsets of the cloud of points. Then, we apply an Adaboost like strategy to select a collection of features achieving a target accuracy in the detection of correct labeling as a whole. Furthermore, we use these features to generate the labeling of the points in the cloud. We demonstrate the approach on a real dataset obtained from the measurement of gait motion of persons, for which the ground truth labeling has been carried out manually. Results are encouraging, achieving high accuracy in both tasks (correct label detection and label generation) at a reduced computational cost.
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Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
Faugeras, O.: Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, Cambridge (1993)
Heikkila, J., Silven, O.: A four-step camera calibration procedure with implicit image correction. In: Proceeding CVPR 1997 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR 1997) p. 1106 (1997)
Lunardi Flam, D., Pacheco de Queiroz, D., Louise Alves de Souza Ramos, T., de Albuquerque Araújo, A., Victor Boechat Gomide, J.: OpenMoCap: an open source software for optical motion capture. In: 2009 VIII Brazilian Symposium on Games and Digital Entertainment, vol. 0, pp. 151–161 (2009)
Guerra-Filho, P.G.: Optical motion capture: theory and implementation. J. Theor. Appl. Inform. (RITA) 12(2), 61–89 (2005)
Yu, Q., Li, Q., Deng, Z.: Online motion capture marker labeling for multiple interacting articulated targets. In: Computer Graphics Forum, vol. 26, no. 3, pp. 477–483. Blackwell Publishing Ltd., September 2007
Mehling, M.: Implementation of a low cost marker based infrared light optical tracking system. Kaufmann, H., Institute for Software Technology & Interactive Systems (2006)
Vaughan, C.L., Davis, B.L., Connor, J.C.: Dynamics of Human Gait. Kiboho Publishers, Cape Town (1999)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Cutti, A.G., Ferrari, A., Garofalo, P., Raggi, M., Cappello, A., Ferrari, A.: ‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors. Med. Biol. Eng. Comput. 48(1), 17–25 (2010)
Ferrari, A., Cutti, A.G., Garofalo, P., Raggi, M., Heijboer, M., Cappello, A., Davalli, A.: First in vivo assessment of “Outwalk”: a novel protocol for clinical gait analysis based on inertial and magnetic sensors. Med. Biol. Eng. Comput. 48(1), 1–15 (2010)
Qiao, M., Cheng, J., Bian, W., Tao, D.: Biview learning for human posture segmentation from 3D points cloud. PLoS ONE 9(1), e85811 (2014). doi:10.1371/journal.pone.0085811
Liu, A.-A., Su, Y.-T., Nie, W.-Z., Yang, Z.-X.: Jointly learning multiple sequential dynamics for human action recognition. PLoS ONE 10(7), e0130884 (2015). doi:10.1371/journal.pone.0130884
Meyer, J., Kuderer, M., Muller, J., Burgard, W.: Online marker labeling for fully automatic skeleton tracking in optical motion capture. In: International Conference on Robotics and Automation (ICRA), pp. 5652–5657 (2014)
Schubert, T., Gkogkidis, A., Ball, T., Burgard, W.: Automatic initialization for skeleton tracking in optical motion capture. In: International Conference on Robotics and Automation (ICRA), pp. 734–739 (2015)
Maycock, J., Rohlig, T., Schröder, M., Botsch, M., Ritter, H.J.: Fully automatic optical motion tracking using an inverse kinematics approach. In: International Conference on Humanoids 2015, pp. 461–466 (2015)
Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logistics Q. 2(1–2), 83–97 (1955)
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Jiménez-Bascones, J.L., Graña, M. (2018). An Ensemble of Weak Classifiers for Pattern Recognition in Motion Capture Clouds of Points. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_21
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DOI: https://doi.org/10.1007/978-3-319-59162-9_21
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