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Geoffrey Samuel PhD Researcher Intelligent Systems and Robotics Research Group (ISR) Creative Technologies University of Portsmouth. Comparison of complex background subtraction algorithms using a fixed camera. Intro.

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Comparison of complex background subtraction algorithms using a fixed camera

Geoffrey Samuel

PhD Researcher

Intelligent Systems and Robotics Research Group (ISR)

Creative Technologies

University of Portsmouth

Comparison of complex background subtraction algorithms using a fixed camera


Intro

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


Hypothesis

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.


The goal

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”


Complex background

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.


Synthetic test data

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.


The experiment

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”


The motions

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.


Creating the test scenarios

Creating the test scenarios

Green Screen

Green Screen with actor

Back Ground

Final Composite

“Ground Truth”


The algorithms

The Algorithms

50

Back Plate Difference

│framei – backplate│>Ts


The algorithms1

The Algorithms

50

Frame Difference

│framei – framei-1│>Ts


The algorithms2

The Algorithms

Approximate median

(x = ( framei- framei-1 – framei-2 . . .framei-n ) > Ts )

→ {background += 1}

→ {background -= 1}


The algorithms3

The Algorithms

k

Mixture of Gaussians

frame(it = μ) = Σi=1ωi,t .ț(μ,o)


Measuring the quality

Measuring the Quality

(0,576)

(768,576)

(0,576)

(768,576)

(0,0)

(768,0)

(0,0)

(768,0)

Compare the Per-Pixel value of

each frame with the “Ground Truth”


Results quality

Results - Quality

Most correct pixels

Most incorrect pixels


Results quality1

Results - Quality


Results speed

Results - Speed

“Fastest” Algorithm

“Slowest “Algorithm


Results speed1

Results - Speed


Results speed2

Results - Speed

...now ignoring the Mixture of Gaussian speed results


Conclusion

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.


Conclusion1

Conclusion

Frame Difference :-

Correctly Removed Complex Background

Incorrectly Removed inside of Subject

Backplate Difference :-

Correctly Identified Subject

Incorrectly kept Complex Background


Taking it further

Taking it further

Theory Framework idea:

ƒ

Frame Difference

Backplate Difference

Complex background removed

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.


Where can this lead

Where can this lead?

  • Application of this technology could be used in:

    • Games

    • Surveillance

    • Mesh reconstruction and silhouette extraction

    • Various computer vision tasks


Any questions

Any Questions?


Acknowledgments

Acknowledgments

UK Engineering and Physical Science Research Council

Seth Benton for his Matlab code


Thank you for your time

Thank you for your time

[email protected]

www.GeoffSamuel.com


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