Abstract
Object tracking is a very important application in the fields of computer vision. In practice, automated tracking systems can rarely meet the required performance. This paper improves the attentional based tracking framework with Adaboost and gaze selection. The object classifier is implemented using the ADA Boosting to recognize digits from the MNIST dataset. At the initial object position Gaze selection is performed. The performance of the framework is evaluated using digit videos generated from the MNIST dataset with clutters. In general, the performance of the framework is robust to changes in motion routes and degree of clutter.
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