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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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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 speed
Results - Speed

“Fastest” Algorithm

“Slowest “Algorithm



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



Acknowledgments
Acknowledgments

UK Engineering and Physical Science Research Council

Seth Benton for his Matlab code



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