The authors present an application of the ensemble learning algorithm in the area of visual tracking and servoing. In particular, they investigate an approach based on the boosting technique for robust visual tracking of color objects in an underwater environment. To this end, they use AdaBoost, the most common variant of the boosting algorithm, to select a number of low-complexity but moderately accurate color feature trackers and they 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 their underwater robot. They present quantitative cross-validation results of their visual tracker, and conclude by pointing out some difficulties faced and subsequent shortcomings in the experiments they performed, along with directions of future research on the area of ensemble tracking in real-time.
A boosting approach to visual servo-control of an underwater robot
Ein Boosting-Ansatz für die visuelle Servoregelung eines Unterwasserroboters
2009
12 Seiten, 10 Bilder, 15 Quellen
Aufsatz (Konferenz)
Englisch
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