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Geoffrey Samuel PhD Researcher Intelligent Systems and Robotics Research Group (ISR) Creative Technologies University of Portsmouth

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Intro Background subtraction is a important and vital step for computers to understand and interpreter a real-world scene It allows a computer to ignore a background so to concentrate on a foreground object

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Hypothesis Each background subtraction algorithm will have its advantages and disadvantages, and that looking and comparing these with a real-world situation, it would be possible to pick one algorithm or a method of combining algorithms to produce a algorithm capable of balancing speed with quality.

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera The Goal Test and evaluate the quality and speed of existing background subtraction algorithms on a complex background with different everyday motions, and to compare the results with those of the extracted Ground Truth

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Complex Background Static Background:- Background does not contain any secondary unwanted motion. Controlled environment. Complex Background:- Background contains secondary unwanted motion such as the winds effect on trees or blinds. Real-world data.

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Synthetic Test Data Advantages: Automatically got the Ground Truth. More control over each test clip. Disadvantages: Manual frame by frame Ground Truth extraction. Added artefacts from the Chroma keying and compositing.

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera The Experiment To Create a set of synthetic data with the Ground Truth To test different motions with each background subtraction algorithm To Compare the results of each algorithm with that of the Ground Truth

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera The Motions 7 everyday motions were chosen: Drinking Jogging Picking up wallet Scratching head Sitting down Standing up Walking Each motion started on the left of the screen and concluded on the right.

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Creating the test scenarios Green Screen Back Ground Green Screen with actor Final Composite Ground Truth

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Back Plate Difference frame i – backplate>T s The Algorithms 50

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Frame Difference frame i – frame i-1 >T s The Algorithms 50

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Approximate median (x = ( frame i - frame i-1 – frame i-2... frame i-n ) > T s ) {background += 1} {background -= 1} The Algorithms

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Mixture of Gaussians frame(i t = μ) = Σ i=1 ω i,t. (μ,o) The Algorithms k

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Measuring the Quality Compare the Per-Pixel value of each frame with the Ground Truth (0,0)(768,0) (768,576)(0,576) (0,0)(768,0) (768,576)(0,576)

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Results - Quality Test Motions Backplate DifferenceFrame DifferenceApproximate MedianMixture of Gaussian % of image# of pixels% of image# of pixels% of image# of pixels% of image# of pixels Drinking90.78% % % % Jogging88.24% % % % Picking up Wallet91.26% % % % Scratch head88.18% % % % Sitting down88.51% % % % Standing up89.40% % % % Walking88.47% % % % Most correct pixelsMost incorrect pixels

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Results - Quality

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Results - Speed Test Motions Backplate Difference (Average of 100 times) Frame Difference (Average of 100 times) Approximate Median (Average of 100 times)Mixture of Gaussian Drinking Jogging Picking up Wallet Scratch head Sitting down Standing up Walking Fastest AlgorithmSlowest Algorithm

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Results - Speed

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Results - Speed...now ignoring the Mixture of Gaussian speed results

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Conclusion Backplate difference was the fastest and produce the highest results in 4 out of 7 tests. Frame difference was the ONLY algorithm to correctly remove the complex background, but couldn't correctly identify the foreground element.

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Conclusion Frame Difference :- Correctly Removed Complex Background Incorrectly Removed inside of Subject Backplate Difference :- Correctly Identified Subject Incorrectly kept Complex Background

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Taking it further A new method that incorporated both the speed of updating to remove the background and yet the knowledge of the background to properly extract the wanted foreground element. Theory Framework idea: Frame DifferenceBackplate Difference ƒ Complex background removed

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Where can this lead? Application of this technology could be used in: Games Surveillance Mesh reconstruction and silhouette extraction Various computer vision tasks

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Any Questions?

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Acknowledgments UK Engineering and Physical Science Research Council Seth Benton for his Matlab code

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Geoffrey Samuel Comparison of complex background subtraction algorithms using a fixed camera Thank you for your time

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