On-line boosting is one of the most successful on-line algorithms and thus applied in many computervision applications. However, even though boosting, in general, is well known to be susceptible to class-label noise,...
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For face recognition from video streams often cues such as transcripts, subtitles or on-screen text are available. This information could be very valuable for improving the recognition performance. However, frequently...
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Very recently tracking was approached using classification techniques such as support vector machines. The object to be tracked is discriminated by a classifier from the background. In a similar spirit we propose a no...
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ISBN:
(纸本)1904410146
Very recently tracking was approached using classification techniques such as support vector machines. The object to be tracked is discriminated by a classifier from the background. In a similar spirit we propose a novel on-line AdaBoost feature selection algorithm for tracking. The distinct advantage of our method is its capability of on-line training. This allows to adapt the classifier while tracking the object. Therefore appearance changes of the object (e.g. out of plane rotations, illumination changes) are handled quite naturally. Moreover, depending on the background the algorithm selects the most discriminating features for tracking resulting in stable tracking results. By using fast computable features (e.g. Haar-like wavelets, orientation histograms, local binary patterns) the algorithm runs in real-time. We demonstrate the performance of the algorithm on several (publically available) video sequences.
Current projectors can easily be combined to create an everywhere display, using all suitable surfaces in offices or meeting rooms for the presentation of information. However, the resulting irregular display is not w...
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Maximally Stable Extremal Regions (MSERs) are one of the most prominent interest region detectors in computervision due to their powerful properties and low computational demands. In general MSERs are detected in sin...
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Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However, one drawback of such approaches is drifting if an object is not moving over a long period of ti...
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Recently, combining information from multiple cameras has shown to be very beneficial for object detection and tracking. In contrast, the goal of this work is to train detectors exploiting the vast amount of unlabeled...
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This paper introduces a method which provides robust tracking results and accurately segmented object boundaries in short computation time. The first step of the algorithm is to apply a novel edge detector on efficien...
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ISBN:
(纸本)9781901725360
This paper introduces a method which provides robust tracking results and accurately segmented object boundaries in short computation time. The first step of the algorithm is to apply a novel edge detector on efficiently calculated color probability maps in an object-specific Fisher color space. The proposed edge detector exploits context information by finding the maximally stable boundaries of connected regions in threshold results outperforming purely local edge detectors. Finally, based on the estimated edge maps a probabilistic particle filtering framework hypothesizes rigid transformations for initializing an active contour model to provide accurate object segmentations in each frame. Experimental evaluations show that robust tracking results with accurate segmentations are obtained on challenging data sets.
This paper investigates an approach to perform semantic classification in aerial imagery by compactly integrating multiple feature cues, like appearance and 3D height information. We therefore propose a novel techniqu...
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We present a novel design of an augmented reality interface to support indoor navigation. We combine activity-based instructions with sparse 3D localisation at selected info points in the building. Based on localisati...
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