Passive optical motion capture is one of the predominant technologies for capturing high fidelity human motion, and is a workhorse in a large number of areas such as bio-mechanics, film and video games. While most state-of-the-art systems can automatically identify and track markers on the larger parts of the human body, the markers attached to the fingers and face provide unique challenges and usually require extensive manual cleanup. In this work we present a robust online method for identification and tracking of passive motion capture markers attached to non-rigid structures. The method is especially suited for large capture volumes and sparse marker sets. Once trained, our system can automatically initialize and track the markers, and the subject may exit and enter the capture volume at will. By using multiple assignment hypotheses and soft decisions, it can robustly recover from a difficult situation with many simultaneous occlusions and false observations (ghost markers). In three experiments, we evaluate the method for labeling a variety of marker configurations for finger and facial capture. We also compare the results with two of the most widely used motion capture platforms: Motion Analysis Cortex and Vicon Blade. The results show that our method is better at attaining correct marker labels and is especially beneficial for real-time applications. ; QC 20171127


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Real-time labeling of non-rigid motion capture marker sets


    Beteiligte:

    Erscheinungsdatum :

    2017-01-01


    Anmerkungen:

    Scopus 2-s2.0-85032454324



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Real-Time and Marker-Free 3D Motion Capture for Home Entertainment Oriented Applications

    Michoud, Brice / Guillou, Erwan / Briceño, Hector et al. | Springer Verlag | 2007



    EgoCap:egocentric marker-less motion capture with two fisheye cameras

    Rhodin, Helge / Richardt, Christian / Casas, Dan et al. | BASE | 2016

    Freier Zugriff

    Virtual Fashion Show Using Real-Time Markerless Motion Capture

    Okada, R. / Stenger, B. / Ike, T. et al. | British Library Conference Proceedings | 2006


    Autonomous Real-Time Model Building for Optical Motion Capture

    Rajko, S. / Qian, G. | British Library Conference Proceedings | 2005