Signal Processing and Communication Group

This report considers the problem of using marker locations from optical motion capture data to identify and parameterise the underlying human skeleton struc-ture and motion over time. It is concerned with real-time algorithms suitable for use within a visual feedback system. The algorithms presented require 3 markers on each limb segment and can be implemented in a sequential fashion; hence the computational cost of updating the centres of rotation is independent of the number of data points previously available. However, a common problem in mo-tion capture is marker occlusion. Most current methods are only useful for offline processing or become ineffective when a significant proportion of markers are missing for a long period of time. This report presents an integrated framework which predicts the occluded marker positions using a Kalman filter in combina-tion with inferred information from neighbouring markers and thereby maintains a continuous data-flow. The results are accurate and reliable even in cases where all markers on a limb segment are occluded, or one or two markers are non-visible for a large sequence of frames. Moreover, it investigates several extensions of the proposed methodology for real-time applications using a sophisticated variable turn model, together with rotor prediction and unscented theory for filtering. It compares each individual method according to its accuracy, complexity and pro-cessing time and suggests the optimal solution for each case. Pre-defined models are not required and skeleton fitting to this complete data can then be updated in real-time. i i