We address the problem of multi-view multi-target tracking using multiple stationary cameras in the application of hockey tracking and test the approach with data from two cameras. The system is based on the previous work by Okuma et al. [50]. We replace AdaBoost detection with blob detection in both image coordinate systems after background subtraction. The sets of blob-detection results are then mapped to the rink coordinate system using a homography transformation. These observations are further merged into the final detection result which will be incorporated into the particle filter. In addition, we extend the particle filter to use multiple observation models, each corresponding to a view. An observation likelihood and a reference color model are also maintained for each player in each view and are updated only when the player is not occluded in that view. As a result of the expanded coverage range and multiple perspectives in the multi-view tracking, even when the target is occluded in one view, it still can be tracked as long as it is visible from another view. The multi-view tracking data are further processed by trajectory smoothing using the Maximum a posteriori smoother. Finally, automatic camera selection is performed using the Hidden Markov Model to create personalized video programs.

In the field of spatial statistics, it is often desirable to generate a smooth surface for a region over which only noisy observations of the surface are available at some locations, or even across time. Kriging and kernel estimations are two of the most popular methods. However, these two methods become problematic when the domain is not regular, such as when it is rectangular or convex. Bivariate B-splines developed by mathematicians provide a useful nonparametric tool in bivariate surface modeling. They inherit several appealing properties of univariate B-splines and are applicable in various modeling problems. More importantly, bivariate B-splines have advantages over kriging and kernel estimation when dealing with complicated domains. The purpose of this dissertation is to develop a nonparametric surface fitting method by using bivariate B-splines that can handle complex spatial domains. The dissertation consists of four parts. The first part of this dissertation explains the challenges of smoothing over complicated domains and reviews existing methods. The second part introduces bivariate B-splines and explains its properties and implementation techniques. The third and fourth parts discuss application of the bivariate B-splines in two nonparametric spatial surface fitting problems. In particular, the third part develops a penalized B-splines method to reconstruct a smooth surface from noisy observations. A numerical algorithm is derived, implemented, and applied to simulated and real data. The fourth part develops a reduced rank mixed-effects model for functional principal components analysis of sparsely observed spatial data. A numerical algorithm is used to implement the method and tested on simulated and real data.

The policy instrument of central banks everywhere has usually been a short-term nominal interest rate. This means that central banks have adopted operating procedures whose goal has been to produce some desired level of money market interest rates. Although the Bank of England was in many respects the pioneer of these operating procedures, theoretical and empirical attention has focused almost exclusively on the Federal Reserve. This thesis aims to redress this imbalance by examining - in detail - the sterling money market and the operations of the Bank of England. This task is carried out in two parts. Part I reviews central banks' use of the interest instrument more generally, beginning with an historical sketch of the evolution of central bank money market operations. This sketch is complemented by a critical discussion of two important concepts relating to such operations, namely interest rate smoothing and money base control. A simple analytical model is then developed to illustrate the determination of money market interest rates by the central bank. Part II specifically concerns the money market operations of the Bank of England, and their implications for the behaviour of sterling money market interest rates. First, a model of the term structure of money market interest rates is derived. Its predicted behaviour in reaction to a change in the Bank's official rate is then empirically verified. Next, the yield on eligible bills - the Bank's intervention asset - is examined. It is argued that these assets carry an excess liquidity premium, arising from the Bank's constraints on their issue. Finally, an empirical model of the overnight interest rate - the UK equivalent of the federal funds rate - is developed, and the reasons for its volatility are investigated.

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We address the problem of multi-view multi-target tracking using multiple stationary cameras in the application of hockey tracking and test the approach with data from two cameras. The system is based on the previous work by Okuma et al. [50]. We replace AdaBoost detection with blob detection in both image coordinate systems after background subtraction. The sets of blob-detection results are then mapped to the rink coordinate system using a homography transformation. These observations are further merged into the final detection result which will be incorporated into the particle filter. In addition, we extend the particle filter to use multiple observation models, each corresponding to a view. An observation likelihood and a reference color model are also maintained for each player in each view and are updated only when the player is not occluded in that view. As a result of the expanded coverage range and multiple perspectives in the multi-view tracking, even when the target is occluded in one view, it still can be tracked as long as it is visible from another view. The multi-view tracking data are further processed by trajectory smoothing using the Maximum a posteriori smoother. Finally, automatic camera selection is performed using the Hidden Markov Model to create personalized video programs.

Thesis (Ph.D.) - University of St Andrews, January 2007.

Thesis (M.S.)--Ohio University, November, 1995. / Title from PDF t.p.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [104]-115).

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 138-151).

Thesis (M. Phil.)--University of Hong Kong, 1999. / Includes bibliographical references (leaves 79-86).