Signal Processing and Communications Group

This work addresses the problem of real-time joint localisation of legged skeletons in the presence of missing data. The data is assumed to be labelled 3d marker positions from a motion capture system.n integrated framework is presented which predicts the occluded marker positions using a variable turn model within an Unscented Kalman filter. Inferred information from neighbouring markers is used as observation states; these constraints are efficient, simple and real-time implementable. This work also takes advantage of the common case that missing markers are often visible to only a single camera, resulting in more accurate predictions. An Inverse Kinematics technique is then applied ensuring that the bone lengths remain constant over time; the system can thereby maintain a continuous data-flow. The marker and Centre of Rotation (CoR) positions can be calculated with high accuracy even in cases where markers are occluded for a long period of time. Our methodology is tested against some of the most popular methods for marker prediction and the results confirm that our approach outperforms these methods in estimating both marker and CoR positions.

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Inverse Kinematics is defined as the problem of determining a set of appropriate joint configurations for which the end effectors move to desired positions as smoothly, rapidly, and as accurately as possible. However, many of the currently available methods suffer from high computational cost and production of unrealistic poses. In this work, a novel heuristic method, called Forward And Backward Reaching Inverse Kinematics (FABRIK), is described and compared with some of the most popular existing methods regarding reliability, computational cost and conversion criteria. FABRIK avoids the use of rotational angles or matrices, and instead finds each joint position via locating a point on a line. Thus, it converges in fewer iterations, has low computational cost and produces visually realistic poses. Constraints can easily be incorporated within FABRIK and multiple chains with multiple end effectors are also easily supported.

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Articulated hand tracking systems have been commonly used in virtual reality applications, including systems with human-computer interaction or interaction with game consoles. However, building an effective real-time hand pose tracker remains challenging. In this work, we present a simple and efficient methodology for tracking and reconstructing 3d hand poses using a markered optical motion capture system. Markers were positioned at strategic points, and an inverse kinematics solver (FABRIK) was incorporated to fit the rest of the joints to the hand model. The model is highly constrained with physiological constraints, allowing motion only within a feasible set. The method is real-time implementable and the results are smooth, even with a low frame rate.

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Geometric Algebra (GA) provides a convenient mathematical notation for representing orientations and rotations of objects in three dimensions. The Conformal model of GA give us the ability to describe algorithms in a geometrically intuitive and compact manner since basic entities, such as spheres, lines, planes and circles, are simply represented by algebraic objects. GA is also more numerically stable and more efficient than rotation matrices making it popular for applications in computer graphics and robotics.

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