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Image Processing Laboratory
   Yuichi Yaguchi Laboratory
     s1170133 Keigo Amma
Introduction
 In the scene of computing, we can see
  various gesture interface for standard
  family use. such as mouse, remote
  controller for TV, kinect, etc.
 There are many techniques to detect
  human gesture or posture for
  recognizing each gesture.
Background, related work
   Gesture detect with image processing
     Model base
      ○ DP,particle filter, kalman filter
     Feature base
      ○ SIFT, Harris corner,Hog
     Machine learning
      ○ Boosting(Ada boost),SVM
    There are three genarally field.

    Many of existing method have week point that not
    strong occlusion, difficult malti-detection, need to
    combine another method etc…
Motivation
   I want to make useful interface and confirm
    performance metohd of the interface.

   I use method Time-Space Cointinuous
    Dynamic Programming(TSCDP) to make
    interface    (T.Matsuzaki (2012) MIRU)


   My thesis problem is to evaluate the
    method performance by experiment with
    implemented gesture interface
What’s TSCDP
    TSCDP, which short for Time-Space
     Continuous Dynamic Programming
    The algorithm detect specific motion which
     given by movie.
    Strong occlusion, multi-tracking
    movie

               …
                                                  Point and Time
                         INPUT   TSCDP   OUTPUT   of existing
                                                  trajectory
trajectory(point,color
)
Experiment, Environment
   Envioronment
     One USB Web camera
     1 Frame size 40x30 (now adjusting…)
     Usual complex back ground(My lab back
      ground)
   Experiment
     Gesture(right, left, up, down, circle) detection
      rate.
     Each experiment test 5 people
     Try 100 each gesture
     Case have a occlusion.
Result(not yet)
   Gesture rate
       Right    ???/100
       Left     ???/100
       Up       ???/100
       Down     ???/100
       Circle   ???/100
   Gesture rate(Occlusion)
       Right    ???/100
       Left     ???/100
       Up       ???/100
       Down     ???/100
       Circle   ???/100
Future work
 TSCDP Not only gesture tracking
 Combination TSCDP and another
  method For tracking
 Compare other gesture detection
 Improve TSCDP gesture Interface

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Thesispresen2

  • 1. Image Processing Laboratory Yuichi Yaguchi Laboratory s1170133 Keigo Amma
  • 2. Introduction  In the scene of computing, we can see various gesture interface for standard family use. such as mouse, remote controller for TV, kinect, etc.  There are many techniques to detect human gesture or posture for recognizing each gesture.
  • 3. Background, related work  Gesture detect with image processing  Model base ○ DP,particle filter, kalman filter  Feature base ○ SIFT, Harris corner,Hog  Machine learning ○ Boosting(Ada boost),SVM There are three genarally field. Many of existing method have week point that not strong occlusion, difficult malti-detection, need to combine another method etc…
  • 4. Motivation  I want to make useful interface and confirm performance metohd of the interface.  I use method Time-Space Cointinuous Dynamic Programming(TSCDP) to make interface (T.Matsuzaki (2012) MIRU)  My thesis problem is to evaluate the method performance by experiment with implemented gesture interface
  • 5. What’s TSCDP  TSCDP, which short for Time-Space Continuous Dynamic Programming  The algorithm detect specific motion which given by movie.  Strong occlusion, multi-tracking movie … Point and Time INPUT TSCDP OUTPUT of existing trajectory trajectory(point,color )
  • 6. Experiment, Environment  Envioronment  One USB Web camera  1 Frame size 40x30 (now adjusting…)  Usual complex back ground(My lab back ground)  Experiment  Gesture(right, left, up, down, circle) detection rate.  Each experiment test 5 people  Try 100 each gesture  Case have a occlusion.
  • 7. Result(not yet)  Gesture rate  Right ???/100  Left ???/100  Up ???/100  Down ???/100  Circle ???/100  Gesture rate(Occlusion)  Right ???/100  Left ???/100  Up ???/100  Down ???/100  Circle ???/100
  • 8. Future work  TSCDP Not only gesture tracking  Combination TSCDP and another method For tracking  Compare other gesture detection  Improve TSCDP gesture Interface