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- 1. Background Subtraction Shashank Dhariwal Manmeet Singh Kapoor Adham Beyki 1
- 2. Agenda Introduction Challenges Various Techniques &Comparison Mixture of Gaussians Advantages & Disadvantages Implementation & Results References 2
- 3. Introduction A computational vision process Identifies frame A moving objects from the video class 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 scene’s static background. | framei – backgroundi| > Th 4
- 5. Challenges Illumination Changes Motion Changes Changes in Background Geometry • Gradual • Sudden • Camera Oscillations • High Frequency Objects • Parked cars… 5
- 6. Various Techniques Running Gaussian Average Temporal Median Filter Mixture of Gaussians(MoG) Kernel Density Estimation(KDE) Sequential Kernel Density Approximation(SKDA) Co-occurrence of Image Variance Eigenbackgrounds 6
- 7. Comparison Method Speed Memory Accuracy I I Low-Medium Temporal Median Filter ns ns Low-Medium Mixture of Gaussians m m High Kernel Density Estimation n n High Sequential KD Approximation m+1 m Medium-High Co-occurrence of Image Variance 8n/N2 nK/N2 Medium M n Medium Running Gaussian Average Eigenbackgrounds Table 1 – Comparison [2] 7
- 8. Mixture of Gaussian Deals with • Repeated motion • Background clutter • Long term scene change 8
- 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 scene 2. Modelling each pixel using Kalman filter -Not robust to backgrounds with repetitive change -Takes significant time to re-establish the background 3. Modelling each pixel using single Gaussian -Good indoor performance - Not good enough out-door scenes for repetitive change 9
- 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 Gaussian Fig 1 . [ 3 ] 11
- 12. Background Subtraction -MoG Pixel process : It’s the history of the pixel ‘X’’s 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 matrix 12
- 13. Background Subtraction -MoG Decision making : For each Gaussian in the mixture of pixel X if pixel X <= 2.5 (probability of .996) standard deviation form the mean then the Gaussian is said to be ‘matched’ - Increase the weight - Adjust the mean closer to X(t) - Decrease the Variance Else the Gaussian is ‘unmatched’ - Decrease the weight If all the Gaussians in the mixture for pixel X(t) are unmatched - Mark X(t) as foreground pixel - Find the least probable Gaussian in the mixture and replace it with a new Gaussian with the following parameters: - Mean = X(t) i.e present value of X - Variance as a high value - Weight as a low value 13
- 14. Background Subtraction -MoG Part 2 : Updating of parameters and using a suitable heuristic for distributions that represent background pixels Based on the decision made , change the following parameters using the equations given below: Where - α (alpha) is the learning parameter. - M (i,t) value is set to 1 for model that matched and 0 for rest - µ(mean) and σ (Std Deviation) for unmatched remain same and changes for the matched distributions - ρ is the updating parameter 14
- 15. Background Subtraction -MoG Advantages : Robust against movement that are part of background, e.g moving branches of a tree Robust against rain , snow, etc…. Disadvantages: Not a good subtraction when shadows are there Difficulty with objects overlapping Fast lighting changes were also an issue. Gives false positives 15
- 16. Implementation Outdoor Scene Moving cars Stopped cars Pedestrians Luminosity changes Shadows Leaves and branches of trees 16
- 17. Implementation Frame Difference as the easiest method o Objects with uniformly distributed intensity o Objects must be moving all the time! Computationally cheap Highly adaptive background model Tuning threshold value (=25 for our example) 17
- 18. Implementation MoG a complex method o Parametric model o Mixture of Gaussian components o Comparing pixel value with tracking Gaussian components Very good at separating objects Suppressing background noise Parameter optimisation Not quickly enough adaptive background model 18
- 19. Implementation 19
- 20. References 1. 2. 3. 4. 5. 6. 7. Tarun Baloch, MSc Thesis ‘Background Subtraction in Highly Illuminated Indoor Environment’ Indian Institute of Technology, 2010 M. Piccardi. Background Subtraction techniques : A Review . In IEEE International Conference on Systems, Man and Cybernetics, 2004, Volume 4, pages 3099– 3104, 2005. URL http://dx.doi.org/10.1109/ICSMC.2004.1400815. Eric Thul , ECSE-626 Final Project: An evaluation of Chris Stauffer and W. E. L. Grimson’s method for background subtraction, 2008, www.cs.mcgill.ca/~ethul/pub/course/ecse626/project-report.pdf C. Stauffer and W. E. L. Grimson. Adaptive background mixture models for realtime tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, 2:252, 1999. URL http://doi.ieeecomputersociety.org/10.1109/CVPR.1999.784637. A A Mazeed, Mark Nixon and Steve Gunn, Classifiers Combination for Improved Motion Segmentation, 2004,eprints.ecs.soton.ac.uk › ECS › Research › Publications Omar Javed, Khurram Shafique and Mubarak Shah, A hierarchical approach to robust background subtraction using colour and gradient information,2002, visionnas2.cs.ucf.edu/papers/javed_wmvc_2002.pdf Sen-Ching S.Chung and Chandrika Kamath, Robust techniques for background subtraction in urban traffic video, 2004, www.llnl.gov/casc/sapphire/pubs/UCRL-CONF-200706.pdf 20

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