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    McGill University, School of Computer Science, Montreal, Canada

    A Boosting Approach to Visual Servo-Control of an Underwater Robot

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    Summary. 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. 1 Problem Statement We investigate the application of machine learning algorithms, ensemble learning in particular, to visual tracking and vision-based robot control. This work looks at using machine learning to create a robust

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    Title : A Boosting Approach to Visual Servo-Control of an Underwater Robot
    Abstract : Summary. 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. 1 Problem Statement We investigate the application of machine learning algorithms, ensemble learning in particular, to visual tracking and vision-based robot control. This work looks at using machine learning to create a robust
    Subject : unspecified
    Area : Computer Science
    Language : English
    Affiliations
    Url : http://www.cim.mcgill.ca/~mrl/pubs/junaed/iser2008final.pdf
    Doi : 10.1.1.147.1128

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