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We propose a real-time algorithm for the generic classification of humans and objects in 3D scenes. The algorithm does not depend on color information and works with depth data alone, making it very flexible for a wide area of applications. Further, we will show that it is very resistant to occlusion and will give correct classification results even in cases, where only a fraction of a full human...
Fast vision-based object and person tracking is important for various applications in mobile robotics and Human-Robot Interaction. While current state-of-the-art methods use descriptive features for visual tracking, we propose a novel approach using a sparse template based feature set, which is drawn from homogeneous regions on the object to be tracked. Using only a small number of simple features,...
Fast and robust person reidentification is an important task in multi-camera surveillance and automated access control. We present an efficient appearance-based algorithm, able to reidentify a person regardless of occlusions, distance to the camera, and changes in view and lighting. The use of fast online feature selection techniques enables us to perform reidentification in hyper-real-time for a...
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