Human Gestures Recongition

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Research in human gestures recognition

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Human Gestures Recongition

  1. 1. Research Progress in Human Gestures Recognition By Ahmed Ibrahim 13/5/2011
  2. 2. Last Meeting Discussion Points Our research considers the task of labeling videos containing human motion with action classes and distinguishes between normal and abnormal actions. Using manifolds as a model is an accepted approach for representing human actions. Distinguishing between actions of different classes and generalizing variations within one action class to detect the abnormality. Finding benchmark Human Action datasets
  3. 3. KTH Human Actions dataset It contains six types of human actions (walking, jogging, running, boxing, hand waving and hand clapping) performed several times by 25 subjects in four different scenarios The background is relatively static.
  4. 4. Weizmann Human Actions dataset This dataset contains nine different people each performing ten natural actions such as “run,” “walk,” “skip,” “jump”, “jump forward on two legs”, “jump in place on two legs”, “wave two hands”, “wave one hand”. The background is static.
  5. 5. Proposed Approach
  6. 6. By background subtraction and tracking the location of the first catch out frames Region of interest will be skeletonised Interested feature points which generates motion patterns Feature Set By background subtraction and tracking the location of the first catch out frames Detected active object Shape (contour) Features of active contour will generates motion patterns
  7. 7. Questions!

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