ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS

2017 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Meenal Suryakant Vatsaraj ◽  
Rajan Vishnu Parab ◽  
D S Bade
Keyword(s):  
Anomaly Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Background Subtraction ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Anomalous Behavior ◽  
Markov Random ◽  
Wide Range ◽  
Spatio Temporal

Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on a histogram of oriented gradients and Markov random field easily captures varying dynamic of the crowded environment.Histogram of oriented gradients along with well-known Markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost.To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation. 

ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS

2017 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Meenal Suryakant Vatsaraj ◽  
Rajan Vishnu Parab ◽  
Prof.D.S Bade
Keyword(s):  
Anomaly Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Background Subtraction ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Histogram Of Oriented Gradients ◽  
Markov Random ◽  
Wide Range ◽  
Crowded Environment

Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on histogram of oriented gradients and markov random field easily captures varying dynamic of the crowded environment. Histogram of oriented gradients along with well known markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost. To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation.

fMRI Signal Restoration Using a Spatio-Temporal Markov Random Field Preserving Transitions

NeuroImage ◽  
1998 ◽  
Vol 8 (4) ◽  
pp. 340-349 ◽  
Author(s):  
Xavier Descombes ◽  
Frithjof Kruggel ◽  
D.Yves von Cramon
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Signal Restoration ◽  
Fmri Signal ◽  
Markov Random ◽  
Spatio Temporal

A one-step Bayesian inversion framework for three-dimensional reservoir characterization based on a Gaussian mixture model – A Norwegian Sea demonstration

Geophysics ◽  
2020 ◽  
pp. 1-68
Author(s):  
Torstein Fjeldstad ◽  
Per Avseth ◽  
Henning Omre
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Gaussian Mixture ◽  
Three Dimensions ◽  
Monte Carlo Algorithm ◽  
Petrophysical Properties ◽  
Norwegian Sea ◽  
Spatial Coupling ◽  
Markov Random ◽  
One Step

A one-step approach for Bayesian prediction and uncertainty quantification of lithology/fluid classes, petrophysical properties and elastic attributes conditional on prestack 3D seismic amplitude-versus-offset data is presented. A 3D Markov random field prior model is assumed for the lithology/fluid classes to ensure spatially coupled lithology/fluid class predictions in both the lateral and vertical directions. Conditional on the lithology/fluid classes, we consider Gauss-linear petrophysical and rock physics models including depth trends. Then, the marginal prior models for the petrophysical properties and elastic attributes are multivariate Gaussian mixture models. The likelihood model is assumed to be Gauss-linear to allow for analytic computation. A recursive algorithm that translates the Gibbs formulation of the Markov random field into a set of vertical Markov chains is proposed. This algorithm provides a proposal density in a Markov chain Monte Carlo algorithm such that efficient simulation from the posterior model of interest in three dimensions is feasible. The model is demonstrated on real data from a Norwegian Sea gas reservoir. We evaluate the model at the location of a blind well, and we compare results from the proposed model with results from a set of 1D models where each vertical trace is inverted independently. At the blind well location, we obtain at most a 60 % reduction in the root mean square error for the proposed 3D model compared to the model without lateral spatial coupling.

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