Visual Analysis of Articulated Motion

The ability of machines to recognise and interpret human action and gesture from standard video footage has wide-ranging applications for control, analysis and security. However, in many scenarios the use of commercial motion capture systems is undesirable or infeasible (e.g. intelligent surveillance). In particular, commercial systems are restricted by their dependence on markers and the use of multiple cameras that must be synchronized and calibrated by hand. It is the aim of this thesis to develop methods that relax these constraints in order to bring inexpensive, off-the-shelf motion capture several steps closer to a reality. In doing so, we demonstrate that image projections of important anatomical landmarks on the body (specifically, joint centre projections) can be recovered automatically from image data. One approach exploits geometric methods developed in the field of Structure From Motion (SFM), whereby point features on the surface of an articulated body impose constraints on the hidden joint locations, even for a single view. An alternative approach explores Machine Learning to employ context-specifi

TRESADERN et al.: ADDITIVE UPDATE PREDICTORS IN AAMS 1 Additive Update Predictors in Active Appearance Models

The Active Appearance Model (AAM) provides an efficient method for localizing objects that vary in both shape and texture, and uses a linear regressor to predict updates to model parameters based on current image residuals. This study investigates using additive (or ‘boosted’) predictors, both linear and non-linear, as a substitute for the linear predictor in order to improve accuracy and efficiency. We demonstrate: (a) a method for training additive models that is several times faster than the standard approach without sacrificing accuracy; (b) that linear additive models can serve as an effective substitute for linear regression; (c) that linear models are as effective as non-linear models when close to the true solution. Based on these observations, we compare a ‘hybrid ’ AAM to the standard AAM for both the XM2VTS and BioID datasets, including cross-dataset evaluations.

Simulating acceleration from stereophotogrammetry for medical device design

When designing a medical device based on lightweight accelerometers, the designer is faced with a number of questions in order to maximize performance while minimizing cost and complexity: Where should the inertial unit be located? How many units are required? How is performance affected if the unit is not correctly located during donning? One way to answer these questions is to use position data from a single trial, captured with a nonportable measurement system (e.g., tereophotogrammetry) to simulate measurements from multiple accelerometers at different locations on the body. In this paper, we undertake a thorough investigation into the applicability of these simulated acceleration signals via a series of interdependent experiments of increasing generality. We measured the dynamics of a reference coordinate frame using stereophotogrammetry over a number of trials. These dynamics were then used to simulate several “virtual” accelerometers at different points on the body segment. We then compared the simulated signals with those directly measured to evaluate the error under a number of conditions. Finally, we demonstrated an example of how simulated signals can be employed in a system design application. In the best case, we may expect an error of 0.028 m/ s2 between a derived virtual signal and that directly measured by an accelerometer. In practice, however, using centripetal and tangential acceleration terms (that are poorly estimated) results in an error that is an order of magnitude greater than the baseline. Furthermore, nonrigidity of the limb can increase error dramatically, although the effects can be reduced considerably via careful modeling. We conclude that using simulated signals has definite benefits when an appropriate model of the body segment is applied