68,950 research outputs found

    Human Detection in Video Surveillance System

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    Object detection is a crucial part in today’s video surveillance systems. Many methods have evolved over the years that include Background Subtraction at the pinnacle. Background subtraction is a technique in which the video is segmented in multiple frames. A base frame called as “Background” is used to subtract another frame from it to detect “Foreground”. Motion–based and shape-based algorithms boost the Background subtraction method. The multiple objects detection technique used in surveillance system uses Support Vector Machine (SVM) to detect and classify the different objects. In this project, study proposes a novel object detection and its classification using Support Vector Machine (SVM) which is used to differentiate objects according to the set of points on the objects. The algorithm then aims at the classification of these key-points, namely at discriminating between the points which belongs to objects and all the others, by means of a Support Vector Machine (SVM) classifier. At the end of the procedure, the objects present inside the scene are identified by analyzing at the key-points previously classified as specific object points. It begins with a feature extraction process from which a set of consistent key-points is identified. Being able to identify specific objects or a particular class of objects in an image can provide several advantages and can open the door to the development of various interesting applications. DOI: 10.17762/ijritcc2321-8169.16048

    RPCA-KFE: Key Frame Extraction for Consumer Video based Robust Principal Component Analysis

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    Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content.Comment: This paper has been withdrawn by the author due to a crucial sign error in equation

    Real Time Turbulent Video Perfecting by Image Stabilization and Super-Resolution

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    Image and video quality in Long Range Observation Systems (LOROS) suffer from atmospheric turbulence that causes small neighbourhoods in image frames to chaotically move in different directions and substantially hampers visual analysis of such image and video sequences. The paper presents a real-time algorithm for perfecting turbulence degraded videos by means of stabilization and resolution enhancement. The latter is achieved by exploiting the turbulent motion. The algorithm involves generation of a reference frame and estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame; segmentation of the displacement map into two classes: stationary and moving objects and resolution enhancement of stationary objects, while preserving real motion. Experiments with synthetic and real-life sequences have shown that the enhanced videos, generated in real time, exhibit substantially better resolution and complete stabilization for stationary objects while retaining real motion.Comment: Submitted to The Seventh IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2007) August, 2007 Palma de Mallorca, Spai
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