1 / 14

Real Time Appearance Based Hand Tracking

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

montana
Download Presentation

Real Time Appearance Based Hand Tracking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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|xi) 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|xi) • every pixel –xi • r × c window • color distribution of the hand O in the frame t−1 -- Gaussian • 3×1 mean vector –μO • 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。

More Related