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A Bayesian Approach to Background Modeling
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops, 2006Learning background statistics is an essential task for several visual surveillance applications such as incident detection and traf.c management. In this paper, we propose a new method for modeling background statistics of a dynamic scene. Each pixel is represented with layers of Gaussian distributions.
Oncel Tuzel +2 more
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Multiscale background modelling and segmentation
2009 16th International Conference on Digital Signal Processing, 2009A new multiscale approach to motion based segmentation of objects in video sequences is presented. While image features extracted at multiple scales are commonly used within the pattern recognition community, they have seldom been employed for background modelling and subtraction. The paper describes a methodology for maintaining an explicit background
Dubravko Culibrk +2 more
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Exemplar-based background model initialization
Proceedings of the third ACM international workshop on Video surveillance & sensor networks, 2005Most of the automated video-surveillance applications are based on background (BG) subtraction techniques, that aim at distinguishing moving objects in a static scene. These strategies strongly depend on the BG model, that has to be initialized and updated. A good initialization is crucial for the successive processing.
COLOMBARI, ANDREA +3 more
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Extracting a background image by a multi-modal scene background model
2016 23rd International Conference on Pattern Recognition (ICPR), 2016In scene analysis, the availability of an initial background model that describes the scene without foreground objects is at the basis of many computer vision applications. Multi-modal models of the scene background are frequently adopted in the applications, where each mode tries to keep track of the multiple background modes observed along the ...
Maddalena, Lucia, PETROSINO, Alfredo
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Background of Modelling Approaches and Tools
2019This chapter illustrates the state of the art of the risk analysis methods and models for road tunnels with particular reference to the users’ egress models.
Borghetti F. +3 more
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Background to the Methods, Models and Scenarios
1988In order to characterize the range of severity of moisture conditions in the Stavropol region, a classification of drought has been devised that relates specifically to winter wheat, the most important crop in the area.
Parry, M.L., Carter, T.R., Konijn, N.T.
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2013
Background models are often used in video surveillance systems to find moving objects in an image sequence from a static camera. These models are often built under the assumption that the foreground objects are not known in advance. This assumption has led us to model background using one-class SVM classifiers.
Assaf Glazer +2 more
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Background models are often used in video surveillance systems to find moving objects in an image sequence from a static camera. These models are often built under the assumption that the foreground objects are not known in advance. This assumption has led us to model background using one-class SVM classifiers.
Assaf Glazer +2 more
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Pixel-to-Model background modeling in crowded scenes
2014 IEEE International Conference on Multimedia and Expo (ICME), 2014Background modeling is an important step for many video surveillance applications such as object detection and scene understanding. In this paper, we present a novel Pixel-to-Model (P2M) paradigm for background modeling in crowded scenes. In particular, the proposed method models the background with a set of context features for each pixel, which are ...
Lu Yang 0002 +3 more
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Background Modeling in Video Sequences
2014The background modeling is the very first and essential part of every computer assisted surveillance system. Without it there would be no reliable way for fast and robust detection of moving objects in video sequences. In this paper we collect, describe and compare the main features of the most commonly used techniques of background modeling in video ...
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A Probabilistic Background Model for Tracking
2000A new probabilistic background model based on a Hidden Markov Model is presented. The hidden states of the model enable discrimination between foreground, background and shadow. This model functions as a low level process for a car tracker. A particle filter is employed as a stochastic filter for the car tracker. The use of a particle filter allows the
Jens Rittscher +3 more
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