ICBV Course Final Project

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Real Time Skin Motion Detection

• ICBV Course Final Project
• Arik Krol
• Aviad Pinkovezky

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Motivation

• Current MMI is based mainly on Point Click
  devices
• Video Capturing as a new Approach for MMI
• Hardware is available - Web Cameras and
  powerful
• processors
• Potential Usages Working with laptops, users
  with
• hands on keyboards, etc

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Goals

• Exploring the field of motion detection
• Exploring the field of skin colors distinction
• A working Demo that can detect palm movements
• No Real Time, yet
• Minimal rate of False detections
• Determine Direction of movement

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Motion Detection

• First approach Segmentation by Clustering
  (K-Means)
• Motion Detection by tracking the centers of
  gravity of clusters over the frames
• The Problem Complexity of Calculation, doesnt
  fit into real time scenario!

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Motion Detection (cont.)

• Second approach Subtracting consecutive frames
• Motion Detection by tracking the difference in
  pixels values
• Note - Assumptions are Relatively static
  background and stationary camera

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Motion Detection implementation

• For each two consecutive frames
• Convert from RGB to Grayscale
• Subtraction
• Gaussian Smoothing

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But how to distinguish palm movements???
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Skin Color Detection

• H.S.V Hue, Saturation, Value
• An alternative representation of color pixels
• Enables us to isolate Hue levels, regardless of
  Saturation and Value levels

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Skin Color Detection (cont.)

• The human skin is characterized by different
  levels of red hue - 335 to 25 degrees
• Value level is greater than 40

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Skin Motion Detection

• Motion Pixels Skin Pixels Skin
  Motion Pixels

• Direction of movement Determined by the
  differences of X axis value averages between
  consecutive frames
• Setting adequate thresholds by trial and error

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Skin Motion Detection

• Now, lets try to detect a moving piece of
  paper

• Skin Motion detection results finally in no
  detection at all.

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Problems we encountered

• Face Can create false detection of skin
  movement (head movements non
• skin movement) Solved by tracking the 1/3
  bottom part of the image.
• Complexity of calculation better than
  clustering, yet not real time
• like unsolved
• Skin like objects may cause false detection

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A Few Results
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Future Improvements

• Improving run time performances to support real
  time motion detection, can be achieved by
• Using different programming languages
• Using hardware acceleration (parallel computing,
  GPGPU, etc.)
• Setting thresholds dynamically by calibrating
  the system.
• Identifying a larger variety of movements, and
  adding new features accordingly

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References

• Francesca Gasparini, Raimondo Schettini, Skin
  segmentation using multiple thresholdings
• University of Sussex, UK. Web page of David
  Young, Static Camera and moving
  objectshttp//www.cogs.susx.ac.uk/users/davidy/
  teachvision/vision6.htmlheading3
• And of course, Wikipedia H.S.V

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Questions?