Real-Time Estimation of Missing Markers in Human Motion Capture

This paper considers the problem of taking marker locations from optical motion capture data to identify and parameterise the underlying human skeleton structure and motion over time. It is concerned with real-time algorithms suitable for use within a visual feedback system. A common problem in motion capture is marker occlusion. Most current methods are only useful for offline processing or become ineffective when a significant portion of markers are missing for a long period of time. This paper presents a prediction algorithm, using a Kalman filter approach in combination with inferred information from neighbouring markers, to provide a continuous flow of data. The results are accurate and reliable even in cases where all markers on a limb are occluded, or one or two markers are not visible for a large sequence of frames. Pre-defined models are not required and skeleton fitting to this complete data can then be updated in real-time.