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In this paper we present a novel method for foreground segmentation. Our proposed approach follows a non-parametric background modeling paradigm, thus the background is modeled by a history of recently observed pixel values. The foreground decision depends on a decision threshold. The background update is based on a learning parameter. We extend both of(More)
We generalize the network flow formulation for multiobject tracking to multi-camera setups. In the past, reconstruction of multi-camera data was done as a separate extension. In this work, we present a combined maximum a posteriori (MAP) formulation, which jointly models multicamera reconstruction as well as global temporal data association. A flow graph is(More)
Recognizing people by the way they walk – also known as gait recognition – has been studied extensively in the recent past. Recent gait recognition methods solely focus on data extracted from an RGB video stream. With this work, we provide a means for multimodal gait recognition, by introducing the freely available TUM Gait from Audio, Image and Depth(More)
Using gait recognition methods, people can be identified by the way they walk. The most successful and efficient of these methods are based on the Gait Energy Image (GEI). In this paper, we extend the traditional Gait Energy Image by including depth information. First, GEI is extended by calculating the required silhouettes using depth data. We then(More)
Human gait is an important biometric feature for identification of people. In this paper we present a new dataset for gait recognition. The presented database overcomes a crucial limitation of other state-of-the-art gait recognition databases. More specifically this database addresses the problem of dynamic and static inter object occlusion. Furthermore(More)
This paper presents a unified hierarchical multi-object tracking scheme. The problem of simultaneously tracking multiple objects is cast as a global MAP problem which aims at maximizing the probability of trajectories given the observations in each frame. Directly solving this problem is infeasible, due to computational considerations and the difficulty of(More)
In this paper, we exploit gradient histograms for person identification based on gait. A traditional and successful method for gait recognition is the Gait Energy Image (GEI). Here, person silhouettes are averaged over full gait cycles, which leads to a robust and efficient representation. However, binarized silhouettes only capture edge information at the(More)
INTRODUCTION We developed the iTrainer (iT) as a portable laparoscopic trainer, which incorporates the iPad tablet. We then compared the iT with a standard pelvic trainer (SPT) to assess surgical skills as well as its image quality, resolution, brightness, comfort, and overall performance. MATERIALS AND METHODS We designed and constructed the iT to be(More)
This paper presents advances on the Human ID Gait Challenge. Our method is based on combining an improved gait recognition method with an adapted low resolution face recognition method. For this, we experiment with a new automated segmentation technique based on alpha-matting. This allows better construction of feature images used for gait recognition. The(More)
In 2003, Hofmann and Jost introduced a type system that uses a potential-based amortized analysis to infer bounds on the resource consumption of (first-order) functional programs. This analysis has been successfully applied to many standard algorithms but is limited to bounds that are linear in the size of the input. Here we extend this system to polynomial(More)