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Hands and face tracking for VR applications

Hands and face tracking for VR applications. Javier Varona, Jose’ M. Buades, Francisco J. Perales Unidad de Gra´ficos y Visio´n por Ordenador, Dept. de matematiques i Informatica, Universitat de les Illes Balears (UIB), crta. Valldemossa km. 7,5, 07122 Palma de Mallorca, Spain.

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Hands and face tracking for VR applications

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  1. Hands and face tracking for VR applications Javier Varona, Jose’ M. Buades, Francisco J. Perales Unidad de Gra´ficos y Visio´n por Ordenador, Dept. de matematiques i Informatica, Universitat de les Illes Balears (UIB), crta. Valldemossa km. 7,5, 07122 Palma de Mallorca, Spain Adviser: Chih-Hung Lin Date:2010/12/14 Speaker: Chin He Hsu

  2. Outline 1.Introduction 2. Hands and face tracking algorithm 3.Visualization using H-Anim 4.Conclusion and future work

  3. 1.Introduction • In order to allow a user to navigate in a 3D-space

  4. Interactive 3D-space

  5. system must detect a new user • entering into the system’s environment • analyse him to set parameters • tracking interesting regions

  6. 2. Hands and face tracking algorithm • tracking problem lies in identifying both hands and face in each image • detect skin-colour pixels • data association algorithm

  7. 2.1. Skin-colour segmentation module • skin-colour detection • necessary to model the actor’s skin-colour in a previous step

  8. skin-colour sample • transform these pixels from the RGB-space to HSL-space • hue and saturation values contain the chroma information • two main problems • human skin hue values are near the red colour • saturation values are near 0

  9. skin-colour distribution • Gaussian model

  10. Contours of skin-colour blobs after the connected components process

  11. 2.2. Data association module

  12. Next linear scheme of prediction • that an extreme limb will maintain the same velocity

  13. Set of hypothesis

  14. define an approximation to the distance from the x image pixel to the hypothesis h

  15. calculating the angle • Normalized image pixel and the hypothesis centre

  16. distance between an image pixel and a hypothesis • if d( x ,h)<=0 , then x is inside the hypothesis h ,if d( x ,h)>0 , then x is outside the hypothesis h

  17. a blob with empty intersection with all hypotheses • a pixel x of a blob is inside a limb hypothesis

  18. Occlusion case solved using multiple labelling

  19. 2.3. 3D-point reconstruction

  20. Complete procedure: color segmentation, data association and 3D reconstruction

  21. 3. Visualization using H-Anim • H-Anim (humanoid animation) • we use the H-Anim standard, this way we can collaborate with standard VRML (Virtual Reality Modeling Language ) models

  22. 3D position

  23. 4. Conclusion and future work • proposed a new system • human–computer interaction • future work

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