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Aim of this paper is to propose a solution to the correspondence problem in multi-camera systems. In these systems, two or more cameras are used to record the same scene from different view points. In this way it is possible to face the problem of occlusions in crowding scenes. In this work an object level motion detection algorithm is used and it is applied to the videos sampled by two cameras. The...
Aim of this work is to propose a robust solution to the correspondence problem in multi-camera systems applied to video surveillance. The proposed system merges two different approaches: Self Organizing Map (SOM) and feature based corresponding analysis. The novelty of this work consists of the used approach and the ability to work without the assumption of epipolar geometry. The proposed approach...
This paper presents an efficient real-time method for detecting moving objects in unconstrained environments, using a background subtraction technique. A new background model that combines spatial and temporal information based on similarity measure in angles and intensity between two color vectors is introduced. The comparison is done in RGB color space. A new feature based on chromaticity and intensity...
Video surveillance systems are usually composed of a network of active video sensors that continuously capture the scenes and present them to a human operator for analysis and event detection. Unfortunately human operators are often unable to monitor the video streams coming from a large number of video sensors. In this paper a semantic event detection system based on a neural classifier is presented...
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