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3D View Simulation Based on Face Tracking. Final Presentation for EE7700 DVP Shenghua Wan and Kang zhang May, 2012. Motivation. Multi-touch User Interface Physical Motion Virtual Environment Motion Sensing Game Consoles Wii Kinect. Infrared projector and camera. Infrared sensor bar.

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3d view simulation based on face tracking

3D View Simulation Based on Face Tracking

Final Presentation for EE7700 DVP

Shenghua Wan and Kang zhang

May, 2012

motivation
Motivation
  • Multi-touch User Interface
  • Physical Motion
  • Virtual Environment
  • Motion Sensing Game Consoles
  • Wii
  • Kinect

Infrared projector

and camera

Infrared sensor bar

Infrared LED

objective
Objective
  • Face Motion
  • Tracking
  • Translations
  • Zoom
  • Virtual 3D Scene Explore
  • 3D scene

e.g. a Cube with 8

points with different depth

values.

  • View Simulation
methodology face tracking 1
Methodology-Face Tracking 1
  • Haar Cascade Classifier (Viola & Jones 2001)
  • Haar-like features
  • Integral Image
  • AdaBoost (Freund & Schapire 1995)
  • Cascading
  • Implementation
  • OpenCV
  • Trained Classifier
  • Some Untuned Parameters
methodology face tracking 2
Methodology-Face Tracking 2
  • CAMShift

( Continuously Adaptive Mean-Shift )

  • Assumption

Image histogram of foreground object is time-invariant.

  • Back Projection
  • Mean Shift Algorithm
  • Locate new search window and goto 2
  • Implementation
  • OpenCV
methodology face tracking 2 cont
Methodology-Face Tracking 2(cont)
  • Some comments on CAMShift
  • Sensitive and fast
  • Not Robust
  • tend to be interfered by objects with similar color distribution.

fingers

face

arm

even notebook!

experimental results
Experimental Results
  • Human face moves in real-world
  • Viewpoint moves in the simulated 3D scene as if we are looking at the real-world objects.