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This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formulates the training of Random Forests explicitly as a global loss minimization problem. During training, the losses are minimized via keeping an adaptive weight distribution over the training samples, similar to Boosting methods. In order to keep the method as flexible and general as possible,...
Videos are often associated with additional information that could be valuable for interpretation of their content. This especially applies for the recognition of faces within video streams, where often cues such as transcripts and subtitles are available. However, this data is not completely reliable and might be ambiguously labeled. To overcome these limitations, we take advantage of semi-supervised...
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 time. Thus, the goal of this work is to increase the recall of such classifiers while preserving their accuracy and speed. In particular, this is realized by adapting ideas from Multiple...
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 data given by geometry information of a specific multiple camera setup. Starting from a small number of positive training samples, we apply a co-training strategy in order to generate...
On-line boosting is one of the most successful on-line algorithms and thus applied in many computer vision applications. However, even though boosting, in general, is well known to be susceptible to class-label noise, on-line boosting is mostly applied to self-learning applications such as visual object tracking, where label-noise is an inherent problem. This paper studies the robustness of on-line...
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