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This paper describes a scheme for modelling and tracking the motion of articulated bodies using a number of video cameras. The aim is to obtain complete and accurate information on the three-dimensional location and motion of the bodies over time. Applications include medicine, sports analysis and motion capture for animation. Feature extraction is avoided by placing markers at the joints of the body so that model selection, marker-to-measured-point association, occlusion and the choice of tracking filter are the important issues. While the scheme is general for any number of cameras, emphasis is placed on systems with a small number of cameras where occlusions are a major problem. The system is an amalgamation of new ideas and existing techniques drawn from a variety of disciplines such as machine vision, geometric algebra and radar tracking theory, which have been extended and developed for the marked joints/multiple camera problem. The proposed schemes for modelling and tracking would be easily adapted to markerless motion capture. The paper concludes with examples of the system successfully tracking limb motion using three cameras.

Downhole shocks and vibrations have been identified by many operators as one of the biggest causes of Non-Productive Time, the most significant factors limiting rate of penetration (ROP) and the leading cause of premature failure of downhole tools. Today most of the existing methods for detection and characterization of downhole dynamics rely on costly downhole sensors integrated in bottom hole assembly (BHA). This paper presents a new technique for detecting and characterizing drillstring shock and vibrations in real-time using solely surface measurements and a machine learning method. Using historical offset well data and simulated well data, this new technique provides a method to build a classifying model that can be used during drilling operations to characterize real-time drilling data. Validation of the new technique on recorded data demonstrates the method’s capability to detect and characterize downhole dynamics such as stick-slip and lateral shocks from surface measurements.

Model-based optical motion capture systems require knowledge of the position of the markers relative to the underlying skeleton, the lengths of the skeleton's limbs, and which limb each marker is attached to. These model parameters are typically assumed and entered into the system manually, although techniques exist for calculating some of them, such as the position of the markers relative to the skeleton's joints.We present a fully automatic procedure for determining these model parameters. It tracks the 2D positions of the markers on the cameras' image planes and determines which markers lie on which limb before calculating the position of the underlying skeleton. The only assumption is that the skeleton is made up of rigid limbs connected with ball joints.The proposed system is demonstrated on a number of real data examples and is shown to calculate good estimates of the model parameters in each.

We present a technique for performing the tracking stage of optical motion capture which retains, at each time frame, multiple marker association hypotheses and estimates of the subject's position. Central to this technique are the equations for calculating the likelihood of a sequence of association hypotheses, which we develop using a Bayesian approach. The system is able to perform motion capture using fewer cameras and a lower frame rate than has been used previously, and does not require the assistance of a human operator. We conclude by demonstrating the tracker on real data and provide an example in which our technique is able to correctly determine all marker associations and standard tracking techniques fail.