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Random Forests (RFs) are frequently used in many computer vision and machine learning applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while still achieving state-of-the-art results. However, in most applications RFs are used off-line. This limits their usability for many practical(More)
Online learning has shown to be successful in tracking of previously unknown objects. However, most approaches are limited to a bounding-box representation with fixed aspect ratio. Thus, they provide a less accurate fore-ground/background separation and cannot handle highly non-rigid and articulated objects. This, in turn, increases the amount of noise(More)
Online boosting is one of the most successful on-line learning algorithms in computer vision. While many challenging online learning problems are inherently multi-class, online boosting and its variants are only able to solve binary tasks. In this paper, we present Online Multi-Class LPBoost (OMCLP) which is directly applicable to multi-class problems. From(More)
The paper presents contributions to the design of the Flock of Trackers (FoT). The FoT track-ers estimate the pose of the tracked object by robustly combining displacement estimates from local track-ers that cover the object. The first contribution, called the Cell FoT, allows local trackers to drift to points good to track. The Cell FoT was compared with(More)
Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows how to use unlabeled video sequences, comprising weakly-related object categories towards the target class, to learn better classifiers for tracking and detection. The underlying idea is to exploit(More)
—Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the tree structure in Forests can be replaced by even simpler structures, e.g., Random Naive Bayes classifiers, yielding similar performance. The goal of this paper is to benefit from these findings to(More)
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a. " multi-view learning ". In these methods, usually the classifiers iteratively label each other a subset of the unlabeled data and ignore the rest. In this work, we propose a new(More)
A new method for text line formation for text localization and recognition is proposed. The method exhaustively enumerates short sequences of character regions in order to infer values of hidden text line parameters (such as text direction) and applies the parameters to efficiently limit the search space for longer sequences. The exhaustive enumer-ation of(More)
A successful approach to tracking is to on-line learn dis-criminative classifiers for the target objects. Although these tracking-by-detection approaches are usually fast and accurate they easily drift in case of putative and self-enforced wrong updates. Recent work has shown that classifier-based trackers can be significantly stabilized by applying(More)
Using acoustic detection and classification of vehicles, the proposed autonomous self-learning framework generates scene adaptive vehicle classifiers without the need to hand label any video data. Transport Safety Council (www.etsc.eu), approximately 39,000 people were killed in road collisions in 2008 in Europe. Automated traffic monitoring plays an(More)