We present an application of the ensemble learning algorithm in the area of visual tracking and servoing. In particular, we investigate an
approach based on the Boosting technique for robust visual tracking of color objects in an underwater environment. To this end, we use AdaBoost, the
most common variant of the Boosting algorithm, to select a number of low-complexity but moderately accurate color feature trackers and we combine their
outputs. From a significantly large number of ``weak'' color trackers, the training process selects those which exhibit reasonably good performance (in
terms of mistracking and false positives), and assigns positive weights to these trackers. The tracking process applies these trackers on the input
video frames, and the final tracker output is chosen based on the weights of the final array of trackers. By using computationally inexpensive but
somewhat accurate trackers as members of the ensemble, the system is able to run at quasi-real time, and thus, is deployable on-board our underwater
robot. We present quantitative cross-validation results of our visual tracker, and conclude by pointing out some difficulties faced and subsequent
shortcomings in the experiments we performed, along with directions of future research on the area of ensemble tracking in real-time.