Conference Paper

Selective video transmission by means of virtual reality based object extraction

Dept. de Tecnologia Electronica, Univ. de Malaga, Spain
DOI: 10.1109/MELCON.2004.1346815 Conference: Electrotechnical Conference, 2004. MELECON 2004. Proceedings of the 12th IEEE Mediterranean, Volume: 1
Source: IEEE Xplore


This paper presents a new technique to extract objects from a real complex background so that a video sequence can be decomposed into a set of objects as required for object-oriented video compression techniques. The proposed method is based on a background subtraction technique. However, instead of using a fixed background, the system relies on predicting one from a previously constructed virtual model of the environment. Thus, camera movements are allowed. These movements are estimated by means of a tracker device. We also present the virtual model construction technique for indoor environments. The method has been successfully tested for several different video sequences including capture errors, partially mapped virtual environments and camera positioning errors. Further work will focus on extending the virtual models not only to environment, but also to objects, and integrating the method in a MPEG4 standard compression system.

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    ABSTRACT: This paper presents a new approach to background substraction algorithms to extract video objects from a sequence. Rather than working with a fixed, flat background, the system relies on a virtual 3D model of the background that is automatically created and updated using a sequence of images of the environment. Each time an image is captured, the position of the camera is estimated and the corresponding view of the background can be rendered. The substraction between the frame and the view provides video objects not present in the background. In order to estimate the position of the camera to create the background model and render a background view, artificial landmarks of known size are distributed in the environment. The system works correctly in real environments, over 20 frames per second. It recovers from illumination changes and automatic white balance (AWB) thanks to our background updating algorithm
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