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We propose a representation for scenes containing relocatable objects that can cause partial occlusions of people in a camera's field of view. In many practical applications, relocatable objects tend to appear often; therefore, models for them can be learned offline and stored in a database. We formulate an occluder-centric representation, called a(More)
Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. Model training is accomplished via(More)
State-of-the-art image and action classification systems often employ vocabulary-based representations. The classification accuracy achieved with such vocabulary-based representations depends significantly on the chosen histogram-distance. In particular, when the decision function is a support-vector-machine (SVM), the classification accuracy depends on the(More)
Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. One kernel measures similarity for(More)
Accurate video-based ball tracking in team sports is important for automated game analysis, and has proven very difficult because the ball is often occluded by the players. In this paper, we propose a novel approach to addressing this issue by formulating the tracking in terms of deciding which player, if any, M is in possession of the ball at any given(More)
Current approaches to script identification rely on hand-selected features and often require processing a significant part of the document to achieve reliable identification. We present an approach that applies a large pool of image features to a small training sample and uses subset feature selection techniques to automatically select a subset with the(More)