In this thesis we explore several algorithms for automatic real-time tracking of a tennis ball. We first investigate the use of background subtraction with color/shape recognition for fast tracking of the tennis ball. We then compare our solution with a cascade of boosted Haar classifiers [68] in a simulated environment to estimate the accuracy and ideal processing speeds. The results show that background subtraction techniques were not only faster but also more accurate than Haar classifiers. Following these promising results, we extend the background subtraction and develop other three improved techniques. These techniques use more accurate background models, more reliable and stringent criteria. They allow us to track the tennis ball in a real tennis environment with cameras having higher resolutions and frame rates. <p>We tested our techniques with a large number of real tennis videos. In the indoors environment, We achieved a true positive rate of about 90%, a false alarm rate of less than 2%, and a tracking speed of about 20 fps. For the outdoors environment, the performance of our techniques is not as good as the indoors cases due to the complexity and instability of the outdoors environment. The problem can be solved by resetting our system such that the camera focuses mainly on the tennis ball. Therefore, the influence of the external factors is minimized.<p>Despite the existing limitations, our techniques are able to track a tennis ball with very high accuracy and fast speed which can not be achieved by most tracking techniques currently available. We are confident that the motion information generated from our techniques is reliable and accurate. Giving this promising result, we believe some real-world applications can be constructed.

In this thesis we explore several algorithms for automatic real-time tracking of a tennis ball. We first investigate the use of background subtraction with color/shape recognition for fast tracking of the tennis ball. We then compare our solution with a cascade of boosted Haar classifiers [68] in a simulated environment to estimate the accuracy and ideal processing speeds. The results show that background subtraction techniques were not only faster but also more accurate than Haar classifiers. Following these promising results, we extend the background subtraction and develop other three improved techniques. These techniques use more accurate background models, more reliable and stringent criteria. They allow us to track the tennis ball in a real tennis environment with cameras having higher resolutions and frame rates. <p>We tested our techniques with a large number of real tennis videos. In the indoors environment, We achieved a true positive rate of about 90%, a false alarm rate of less than 2%, and a tracking speed of about 20 fps. For the outdoors environment, the performance of our techniques is not as good as the indoors cases due to the complexity and instability of the outdoors environment. The problem can be solved by resetting our system such that the camera focuses mainly on the tennis ball. Therefore, the influence of the external factors is minimized.<p>Despite the existing limitations, our techniques are able to track a tennis ball with very high accuracy and fast speed which can not be achieved by most tracking techniques currently available. We are confident that the motion information generated from our techniques is reliable and accurate. Giving this promising result, we believe some real-world applications can be constructed.