1. Real Time Appearance Based Hand Tracking The 19th International Conference on Pattern Recognition (ICPR) December 7-11, 2008, Tampa Convention Center, Tampa, FL, USA 報告者：彭成瑋 日期： 2009/12/29 指導教授：陳立祥 教授 實驗室：網際網路多媒體應用實驗室

2. Outline  Introduction  Tracking method  Experiments  Conclusion  Q&A

3. Introduction  Hand tracking is an important problem in the field of human- computer interaction.  Application ： sign language recognition or controlling computer games.  Model-based(3D model) and Appearance-based (Image features)

4. Introduction （ Cont. ）  the hand presents a motion of 27 degrees of freedom (DOF), 21 for the joint angles and 6 for orientation and location[11, 10].  Substantial problems ： out-of-plane rotations scale changes, self-occlusions or segmentation accuracy.  Real-time tracking performance  Maximally Stable Extremal Region (MSER) tracking algorithm.

5. Tracking method  Novel tracking method  Multivariate Gaussians with the Kullback-Leibler distance

6. Color likelihood  calculate a probability value p(O|x i ) for every pixel in the current frame  object-to-be-tracked (hand) O  Kullback-Leibler distance instead of the Bhattacharyya distance  The integral image for Bhattacharyya distance calculation

7. Color likelihood （ Cont. ）  Mahalanobis Distance  Bhattacharyya Distance

8. Color likelihood （ Cont. ）  color likelihood value -- p(O|x i )  every pixel – x i  r × c window  color distribution of the hand O in the frame t−1 -- Gaussian  3×1 mean vector – μ O  3×3 covariance matrix -- Gaussian  multivariate Gaussian --

9. Maximally Stable Extremal Region (MSER) tracking  (a) Input Image (b) Image histogram (c) MSER result

10. Modified MSER tracking  (a) Color likelihood (b) MSER detection result

11. Experiments  25 frames per second on a 320 × 240 video sequences

12. Experiments （ Cont. ）  A simple gesture recognition allows to use the tracker for controlling the mouse pointer and activating mouse- clicks.

13. Conclusion  Novel real time method for tracking hands through image sequences  Efficiently calculated color similarity maps

14. Q&A  Q ：為什麼選擇使用 Appearance-based 來實作.  A ：為了符合即時運算之效能考量，因為 Model-based 使用 3D model 來辨識，需花 費較多運算量。