Computer vision-based analysis of human daily actions using Hidden Markovian Models




Beugeling, Trevor Robert John

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The study of human motion from a medical standpoint has traditionally involved the use of marker-based motion tracking systems, as well as other sensory devices. This equipment is often expensive, has low-portability, and might even influence tracking results by distracting or otherwise inhibiting a subject's normal motion performance. In comparison, non-marker-based tracking methods are less costly, easier to move and set up, and requires no markers or other devices. However, previous work in silhouette-based human motion analysis is typically focused on the classification of activities or the identification of subjects, neither of which are much use to medical professionals. We propose a merging of these two research fields. By applying silhouette-based motion tracking to the problem of motion performance analysis, we have developed a new method which can reliably and accurately model human motions and detect abnormalities. Our approach, which is based on Hidden Markovian Models with continuous observation probabilities, creates standardized models to represent common human motions. These models are then used as a basis for further analysis. We have extensively tested the proposed method with a custom designed database that takes into account speed related and subject related variations of motion performance.



human motion, analysis