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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)
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)
Today video surveillance systems are widely used in public spaces, such as train stations or airports, to enhance security. In order to observe large and complex facilities a huge amount of cameras is required. These create a massive amount of data to be analyzed. It is therefore crucial to support human security staff with automatic surveillance(More)
We present a new spatio-temporal representation for Gait Recognition, which we call Gradient Histogram Energy Image (GHEI). Similar to the successful Gait Energy Image (GEI), information is averaged over full gait cycles to reduce noise. Contrary to GEI, where silhouettes are averaged and thus only edge information at the boundary is used, our GHEI computes(More)
In this paper, we present a multi-modal fusion scheme for tracking and behavior analysis in Smart Home environments. This is applied to tracking multiple people and detecting their behavior. To this end, information from multiple heterogeneous sensors (visual color sensor, thermal sensor, infrared sensor and photonic mixer devices) is combined in a common(More)