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Real Time Skin Motion Detection

Real Time Skin Motion Detection. ICBV Course Final Project Arik Krol Aviad Pinkovezky. 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

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Real Time Skin Motion Detection

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  1. Real Time Skin Motion Detection ICBV Course Final Project Arik Krol Aviad Pinkovezky

  2. 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

  3. 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

  4. 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, doesn’t fit into real time scenario!

  5. 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

  6. Motion Detection implementation: • For each two consecutive frames: • Convert from RGB to Grayscale • Subtraction • Gaussian Smoothing

  7. But how to distinguish palm movements???

  8. 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

  9. 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

  10. 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

  11. Skin Motion Detection: • Now, let’s try to detect a moving piece of paper: • Skin Motion detection results finally in no detection at all.

  12. 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

  13. A Few Results:

  14. 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

  15. References: • Francesca Gasparini, Raimondo Schettini, Skin segmentation using multiple thresholdings • University of Sussex, UK. Web page of David Young, “Static Camera and moving objects”:http://www.cogs.susx.ac.uk/users/davidy/teachvision/vision6.html#heading3 • And of course, Wikipedia – H.S.V

  16. Questions?

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