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This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the label of one example restricts the labeling of the others. We propose a novel paradigm for training a binary classifier from labeled and unlabeled examples that we call P-N(More)
This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object's location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the(More)
This paper proposes a novel method for tracking failure detection. The detection is based on the ForwardBackward error, i.e. the tracking is performed forward and backward in time and the discrepancies between these two trajectories are measured. We demonstrate that the proposed error enables reliable detection of tracking failures and selection of reliable(More)
This work investigates the problem of robust, longterm visual tracking of unknown objects in unconstrained environments. It therefore must cope with frame-cuts, fast camera movements and partial/total object occlusions/dissapearances. We propose a new approach, called Tracking-Modeling-Detection (TMD) that closely integrates adaptive tracking with online(More)
A novel system for long-term tracking of a human face in unconstrained videos is built on Tracking-Learning-Detection (TLD) approach. The system extends TLD with the concept of a generic detector and a validator which is designed for real-time face tracking resistent to occlusions and appearance changes. The off-line trained detector localizes frontal faces(More)
Face detection algorithms based on the work of Viola and Jones [11] train the classifier by processing training examples of face and non-face patterns. A general effort is to process a large number of training examples and hence describe the problem accurately. Current approaches are based on the assumption that non-face patterns can be easily obtained(More)
FUTURE WORK Document the code and make it publically available. Automatic initialization, test different tracker and detector, eliminate planarity assumption, explicitly handle out-of-plane rotation, track multiple targets, learn shape. Our goal is long-term, real-time tracking of arbitrary objects. The object is defined by a region of interest in a single(More)
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