J. Mike McHugh,Janusz Konrad, Venkatesh Saligrama and Pierre-Marc Jodoin Signal Processing Letters,...
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Foreground-Adaptive Background Subtraction
J. Mike McHugh,Janusz Konrad, Venkatesh Saligrama and Pierre-Marc Jodoin Signal Processing Letters, IEEE
Professor: Jar-Ferr YangPresenter: Ming-Hua Tang
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Introduction Background subtraction as a hypothesis test Foreground modeling Makov modeling of change labels Experimental results
Outline
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Change detection based on thresholding intensity differences.
We adapt the threshold to varying video statistics by means of two statistical models.
In addition to a nonparametric background model, we introduce a foreground model based on small spatial neighborhood to improve discrimination sensitivity.
Introduction(1/2)
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We also apply a Markov model to change labels to improve spatial coherence of the detections.
Our approach is using a spatially-variable detection threshold, offers an improved spatial coherence of the detections.
Introduction(2/2)
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Involves two distinct processes that work in a closed loop:
1. Background modeling: a model of the background in the field of view of a camera is created and periodically updated.
2. foreground detection: a decision is made as to whether a new intensity fits the background model; the resulting change label field is fed back into background modeling.
Background subtraction
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At each background location n of k frame , this model uses intensity from recent N frames to estimate background PDF:
is a zero-mean Gaussian with variance that, for simplicity, we consider constant throughout the sequence.
Background modeling
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Change labels can be estimated by evaluating intensity in a new frame at each pixels in current image.
Without an explicit foreground model, is usually considered uniform.
This test is prone to randomly-scattered false positives, even for low θ.
Foreground detection
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We propose a foreground model based on small spatial neighborhood in the same frame.
Let be a change label at n Define a set of neighbors belonging to the
foreground:
Calculate the foreground probability using the kernel-based method
Foreground modeling(1/2)
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At iteration , this results in a refined likelihood ratio test
Since we introduce a positive feedback, the threshold θ must be carefully selected to avoid errors compound.
False negatives will be corrected by Markov model if several neighbors are correctly detected.
Foreground modeling(2/2)
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A pixel surrounded by foreground labels should be more likely to receive a foreground label than a pixel with background neighbors.
Suppose that the label field realization is known for all m except n. Then the decision rule at n is :
By mutually independent spatially on the label field
Makov modeling of change labels
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Since E is a MRF, the a priori probabilities on the right-hand side are Gibbs distributions characterized by the natural temperature γ, cliques c, and potential function V defined on c.
Makov modeling of change labels
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Z and T(γ) are normalization and natural temperature constants respectively.
The potential function, V(c), in the set of all cliques in the image C. In this work, we take C to include all 2-element cliques of the second-order Markov neighborhood.
*Makov modeling of change labels
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Since the labels are binary, we choose to use the Ising potential function
With Z canceled, the ratio of Gibbs priors becomes
*Makov modeling of change labels
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denote the number of foreground and background neighbors of n
γ is selected by the user to control the nonlinear behavior
smaller values of γ strengthen the influence of MRF model on the estimate, while larger values weaken it.
Makov modeling of change labels
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Experimental results
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Experimental results(1/3)
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Experimental results(2/3)
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(b)Probabilities:
(c) followed by
(d) labels computed using additional MRF model.
Experimental results(3/3)