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Improved Hand Tracking System

Improved Hand Tracking System. Jing-Ming Guo , Senior Member, IEEE, Yun -Fu Liu, Student Member, IEEE, Che-Hao Chang, and Hoang-Son Nguyen IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 22, NO. 5, MAY 2012 693. Outline. Introduction Proposed Hand Detection System

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Improved Hand Tracking System

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  1. Improved Hand Tracking System Jing-Ming Guo, Senior Member, IEEE, Yun-Fu Liu, Student Member, IEEE, Che-HaoChang,andHoang-Son Nguyen IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 22, NO. 5, MAY 2012 693

  2. Outline • Introduction • Proposed Hand Detection System • Hand Tracking Methodology • Experimental Results

  3. Introduction • Hand postures are powerful means for communication among humans on communicating. • Many applications are designed by using the motion of hand. • The hand tracking is rather difficult because most of the backgrounds change across frames.

  4. Introduction • Local binary pattern (LBP) [9] is one of the powerful features with low computation. • Chen et al.’s work [10], called Haar-like feature [11], was adopted for hand detection. • This paper proposed to combine the novel pixel-based hierarchical-feature for AdaBoosting(PBHFA), skin color detection, and codebook (CB) foreground detection model to locate a hand in real time.

  5. Proposed PBH Features • AdaBoost is employed to select those few best features from a huge number of features. • The PBH features can significantly reduce the training time for hand detection than normal features.

  6. Proposed PBH Features

  7. Proposed PBH Features

  8. AdaBoosting for Real-Time Hand Detection • where Pt denotes the polarity used for indicating the direction of the inequality. • αt denotes the weight for each weak classifier

  9. HSV Color Space • advantages of this color model in skin color segmentation is that it allows users to intuitively specify the boundary of the hue and saturation. • the hue and saturation are set in between 0° and 5° and 0.23 to 0.68, respectively, as specified in [17].

  10. Foreground Detection • Kim et al. [20] proposed the CB model for foreground detection. • The concept of the CB is to train background pixel pixelwise over a period of time. Sample values at each pixel are clustered as a set of codewords. The combination of multiple codewords can model the mixed backgrounds. • [20]K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real- Time Imag., vol. 11, no. 3, pp. 172–185, Jun. 2005.

  11. Foreground Detection

  12. Foreground Detection • To solve the “still object” problem, a “buffer” is employed to store the history of each tracking target. This buffer is updated frame by frame. • If one target is detected and tracking, the value in buffer associates to the frame that is set as 1.

  13. Foreground Detection

  14. Hand Tracking Methodology • dist1(x, y) < r1.

  15. Experimental Results • In this paper, the public Sebastien Marcel’s hand posture database [15]. • including “A,” “B,” “C,” “Point,” “Five,” and “V”

  16. Experimental Results

  17. Experimental Results

  18. Experimental Results

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