1. Background Subtraction Shashank Dhariwal Manmeet Singh Kapoor Adham Beyki 1

2. Agenda IntroductionChallengesVarious Techniques &ComparisonMixture of GaussiansAdvantages & DisadvantagesImplementation & ResultsReferences2 3. Introduction Acomputational vision process IdentifiesframeAmoving objects from the videoclass of techniques for segmentation[1]3 4. The Problem Aim: Given a frame sequence from a fixed camera, detecting all the foreground objects.Approach: detecting foreground as difference between the current frame & an image of the scenes static background. | framei backgroundi| > Th4 5. Challenges Illumination Changes Motion Changes Changes in Background Geometry Gradual Sudden Camera Oscillations High Frequency Objects Parked cars5 6. Various Techniques RunningGaussian Average Temporal Median Filter Mixture of Gaussians(MoG) Kernel Density Estimation(KDE) Sequential Kernel Density Approximation(SKDA) Co-occurrence of Image Variance Eigenbackgrounds6 7. Comparison MethodSpeedMemoryAccuracyIILow-MediumTemporal Median FilternsnsLow-MediumMixture of GaussiansmmHighKernel Density EstimationnnHighSequential KD Approximationm+1mMedium-HighCo-occurrence of Image Variance8n/N2nK/N2MediumMnMediumRunning Gaussian AverageEigenbackgroundsTable 1 Comparison [2] 7 8. Mixture of GaussianDeals with Repeated motion Background clutter Long term scene change8 9. Mixture of Gaussian Problems with other techniques [3] 1. Averaging images over time -Not robust to scenes with slow moving objects -Single Threshold for entire scene2. Modelling each pixel using Kalman filter -Not robust to backgrounds with repetitive change -Takes significant time to re-establish the background3. Modelling each pixel using single Gaussian -Good indoor performance - Not good enough out-door scenes for repetitive change9 10. Mixture of Gaussian Hypothesis:If we model each pixel as a mixture of Gaussians to determine whether or not a pixel is part of the background, then we will arrive at an effective approach to separate the background and foreground, which can be used for real-time tracking.[ 3] Used in tracking of moving objects: 1. Separating Foreground from Background (our agenda)2. Tracking Objects in Foreground (not in the scope)10 11. Mixture of GaussianFig 1 . [ 3 ] 11 12. Background Subtraction -MoG Pixel process : Its the history of the pixel Xs value from frame 1 to t. [4]Part 1 : For above pixel process of X the probability of observing a X at frame t is as follows,K = no. of Gaussian in the mixture (generally 3-5). [4] weight(i,t) = estimate of the weight of Gaussian in the mixture means (i,t) = mean value of the Gaussian at time t Covariance(i,t) = Covariance matrix of Gaussian in the matrix = Variance * Identity matrix12 13. Background Subtraction -MoG Decision making : For each Gaussian in the mixture of pixel X if pixel X