Abstract
To obtain semantic interpretations of object actions and behaviors is a challenging application of 3D video. This chapter presents a method of computing a kinematic motion description from a 3D video stream of complex human action such as Yoga. In general, appropriate knowledge should be given a priori to obtain a semantic description of physical data. Here a kinematic model is defined as a skeleton structure consisting of bones and joints, while the kinematic description includes characterizations such as how much a joint angle between a pair of connected bones changes over time. The key idea of the presented algorithm is to introduce a pair of reliability measures into the kinematic model matching: surface visibility and photo-consistency measures. The algorithm realizes robust model matching against substantial self-occlusions and enables us to measure 3D kinematic human motion without any markers.
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Matsuyama, T., Nobuhara, S., Takai, T., Tung, T. (2012). Model-Based Complex Kinematic Motion Estimation. In: 3D Video and Its Applications. Springer, London. https://doi.org/10.1007/978-1-4471-4120-4_9
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DOI: https://doi.org/10.1007/978-1-4471-4120-4_9
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