Ilker Gurcan
Tracking objects in a video stream is an important problem in robot learning (learning an object’s visual features from different perspectives as it moves, rotates, scales, and is subjected to some morphological changes such as erosion), defense, public security and many other various domains. In this thesis, we focus on a recently proposed tracking framework […]
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Sang Gu Lee
We implement a real-time image object tracking system with PTZ cameras. In general, mean shift algorithm is efficient for real-time tracking because of its fast and stable performance. However, in the image tracking system for PTZ cameras, the speed is not satisfied. So in this paper, we use parallel mean shift algorithm based on the […]
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Matthew A. Goodrum, Michael J. Trotter, Alla Aksel, Scott T. Acton, Kevin Skadron
This paper presents the parallelization of the particle filter algorithm in a single target video tracking application. In this document we demonstrate the process by which we parallelized the particle filter algorithm, beginning with a MATLAB implementation. The final CUDA program provided approximately 71x speedup over the initial MATLAB implementation.
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Philippe A. Cerfontaine, Marc Schirski, Daniel Bundgens, Torsten Kuhlen
We propose a method to determine the optimal camera alignment for a tracking system with multiple cameras by specifying the volume to be tracked and an initial camera setup. We use optimization strategies based on methods usually employed for solving nonlinear systems of equations. All approaches are fully automatic and take advantage of modern graphics […]
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Brian Clipp, Christopher Zach, Jongwoo Lim, Jan-Michael Frahm, Marc Pollefeys
In this paper we present a real-time simultaneous localization and mapping system which uses a stereo camera as its only input. We combine the benefits of KLT feature tracking, which include high speed and robustness to repetitive features, with wide baseline features, which allow for feature matching after large camera motions. Updating the map of […]
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Yuki Oka, Toshiyuki Kuroda, Tsuyoshi Migita, Takeshi Shakunaga
Tracking 3d pose of a known object is one of the most important problems in computer vision. This paper proposes an appearance-based approach to this problem by combining the sparse template matching and the particle filter. Although the combination of them has already been discussed for 2d tracker, it has not been applied for efficient […]
Nicolas H. Lehment, Moritz Kaiser, Dejan Arsic, Gerhard Rigoll
While monocular gesture recognition slowly reaches maturity, the inclusion of 3D gestures remains a challenge. In order to enable robust and versatile depth-enabled gestures, a depth-image based tracking approach is developed. Using a model-based annealing particle filter approach, the pose of a single subject is retrieved and tracked over longer image and motion sequences. Other […]
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Jakob Santner, Christian Leistner, Amir Saffari, Thomas Pock, Horst Bischof
Tracking-by-detection is increasingly popular in order to tackle the visual tracking problem. Existing adaptive methods suffer from the drifting problem, since they rely on self-updates of an on-line learning method. In contrast to previous work that tackled this problem by employing semi-supervised or multiple-instance learning, we show that augmenting an on-line learning method with complementary […]
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Jan Bandouch, Michael Beetz
We present a markerless tracking system for unconstrained human motions which are typical for everyday manipulation tasks. Our system is capable of tracking a high-dimensional human model (51 DOF) without constricting the type of motion and the need for training sequences. The system reliably tracks humans that frequently interact with the environment, that manipulate objects, […]
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Ingo Schiller, Bogumil Bartczak, Falko Kellner, Reinhard Koch
Mixed reality is the combination of real and virtual scene content. Besides correct alignment of the two modalities and correct occlusion handling the core issues to be tackled are the degree of realism and the ease of use. For a convincing perception correct occlusion handling and shadowing is mandatory. We present a system for mixed […]
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T. Senst, V. Eiselein, R.H. Evangelio, T. Sikora
This paper describes a robust method for the local optical flow estimation and the KLT feature tracking performed on the GPU. Therefore we present an estimator based on the L^2 norm with robust characteristics. In order to increase the robustness at discontinuities we propose a strategy to adapt the used region size. The GPU implementation […]
Ke-Yan Liu, Liang Tang, Shan-Qing Li, Lei Wang, Wei Liu
This paper proposed a parallel particle filter algorithm with the help of GPU (Graphic Processing Unit) in face tracking. Due to illumination and occlusion problems, face tracking usually does not work stably based on a single cue. Three different visual cues, color histogram, edge orientation histogram and wavelet feature, are integrated under the framework of […]
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