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Automatic detection of persons is an important application in visual surveillance. In general, state-of-the-art systems have two main disadvantages: First, usually a general detector has to be learned that is applicable to a wide range of scenes. Thus, the training is time-consuming and requires a huge amount of labeled data. Second, the data is usually processed centralized, which leads to a huge...
To learn an object detector labeled training data is required. Since unlabeled training data is often given as an image sequence we propose a tracking-based approach to minimize the manual effort when learning an object detector. The main idea is to apply a tracker within an active on-line learning framework for selecting and labeling unlabeled samples. For that purpose the current classifier is evaluated...
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