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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)
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 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)
For on-line learning algorithms, which are applied in many vision tasks such as detection or tracking, robust integration of unlabeled samples is a crucial point. Various strategies such as self-training, semi-supervised learning and multiple-instance learning have been proposed. However , these methods are either too adaptive, which causes drifting, or(More)
Tracking and detection of objects often require to apply complex models to cope with the large intra-class variability of the foreground as well as the background class. In this work, we reduce the complexity of a binary classification problem by a context-driven approach. The main idea is to use a hidden multi-class representation to capture(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)
In this paper, we introduce a fully autonomous vehicle classification system that continuously learns from large amounts of unlabeled data. For that purpose, we propose a novel on-line co-training method based on visual and acoustic information. Our system does not need complicated microphone arrays or video calibration and automatically adapts to specific(More)