Improved hand tracking system

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ppt for the ieee paper proposed by Jing-Ming Guo and co..

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Improved hand tracking system

  1. 1. Technical seminar onIMPROVED HAND TRACKING SYSTEM Presented by KISHOR M N Dept. of CSE 2013/3/6 1
  2. 2. Outline Introduction Existing methods Proposed Hand Detection System Ada Boosting for real time Hand detection Foreground detection Hand Tracking Methodology Experimental Results 2013/3/6 2
  3. 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. 2013/3/6 3
  4. 4. Existing methods1) Kolsch and turk proposed a method to detect a hand using image cues and probability distribution. Method used: Pyramid based kanade lucas tomasi feature tracking. Disadv: It cannot classify wooden object that are present in the 2013/3/6 4
  5. 5. Existing methods2) Zhu,yang proposed a method in which each pixel in image is classified as either hand pixel or background pixel. Method used:Hand segmentation based on Bayes decision theory and Gaussian mixture model. Disadv: It cannot distinguish multitargets in an image and error rate 2013/3/6 5
  6. 6. Existing methods3) Binh and Ejima applied skin color tracking for face detection to extract the hand region by separating hand from face. Method used: Skin color tracking and other models. Disadv: It cannot distinguish between skin region or some object with similar color or shape as the hand 2013/3/6 6
  7. 7. Proposed system Local binary pattern (LBP) is one of the powerful features with low computation. Chen et al.’s work called Haar-like feature 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. 2013/3/6 7
  8. 8. Proposed system 2013/3/6 8
  9. 9. Proposed system 2013/3/6 9
  10. 10. 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. 2013/3/6 10
  11. 11. Proposed PBH FeaturesStep 1) Given a training positive and negative samples Xi,Xj of size M × N.Step 2) Process the samples with thehistogram equalization to reduce theinfluence of lighting effect.Step 3) Calculate the average value Ti ofeach sample Xi and use Ti as a thresholdfor binarizing the corresponding Xi toyield a binary image Bi.Step 4) Given Bi, calculate the probability ofblack pixel occurrence at eachposition (x, y) and obtain P(x, y). 2013/3/6 11
  12. 12. Proposed PBH FeaturesStep 5) All possible features Fj in a M×N subwindow can be produced according to the order table O(x, y).Step 6) Finally, these features and the training image Xi and Xj are fed into the AdaBoost algorithm. Each PBH feature can be considered as a weak classifier. 2013/3/6 12
  13. 13. Proposed PBH Features 2013/3/6 13
  14. 14. AdaBoosting for Real-TimeHand Detection Aim of boosting is to improve the classification performance of any given learning algorithm. This phase includes many classifiers;each weak classifier ht is made from a feature ft and threshold Øt where Pt denotes the polarity used for indicating the direction of the inequality. 2013/3/6 14
  15. 15. AdaBoosting for Real-TimeHand Detection• A strong classifier can be obtained by combining the selected weakclassfiers from the AdaBoosting and which canbe recognized as follows 2013/3/6 15
  16. 16. Strong classifier 2013/3/6 16
  17. 17. HSV Color Space A detected hand by the PBH- AdaBoost algorithm is further filtered by skin color to reduce false positive. HSV color model which is robust to lighting change is adopted for skin color localization. Advantages of this color model in skin color segmentation is that it allows users to intuitively specify the boundary of the hue and saturation. 2013/3/6 17
  18. 18. HSV Color Space•The hue and saturation are set inbetween0° and 5° and 0.23 to 0.68, respectively,asspecified in. 2013/3/6 18
  19. 19. Foreground Detection One of the popular method in foreground detection is the mixture of Gaussian(MoG). yet this method has some disadvantages they are overcome by CB model. Kim et al. [2] 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 2013/3/6 19
  20. 20. ForegroundDetection 2013/3/6 20
  21. 21. 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. 2013/3/6 21
  22. 22. Foreground Detection 2013/3/6 22
  23. 23. Hand Tracking Methodology Hand tracking phase is the second step after hand detection. Referenced algorithms can be classified into below categories 1) point tracking 2) kernal tracking 3) silhouette tracking Proposed method uses Euclidean disatnce to track hand. 2013/3/6 23
  24. 24. Hand Tracking Methodology Eucledian distance is calculated as follows dist1(x, y) < r1. 2013/3/6 24
  25. 25. Experimental Results In this paper, the public Sebastien Marcel’s hand posture database [3]. including “A,” “B,” “C,” “Point,” “Five,” and “V” 2013/3/6 25
  26. 26. Experimental Results 2013/3/6 26
  27. 27. Experimental Results 2013/3/6 27
  28. 28. Experimental Results Hand tracking results 2013/3/6 28
  29. 29. Experimental Results 2013/3/6 29
  30. 30. Experimental ResultsPyramidbased KSTfeaturetrackingAppearence basedapproachProposedsystem 2013/3/6 30
  31. 31. Conclusion Hand tracking system was proposed by using the PBHFA, skin color segmentation, and CB model. The goal is to use PBH feature to reduce the required training time and further reduce the required computation in tracking phase. According to the experimental results, the above tasks were achieved, meanwhile the tracking accuracy was still maintained in high level as that of the Haar like feature. 2013/3/6 31
  32. 32. References1) M. Kolsch and M. Turk, “Fast 2D hand tracking with flocks of features and multi-cue integration,” in Proc. CVPRW, vol. 10. Jun. 2004, p. 158.2) 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. 2005.3) Hand Posture Database [Online]. Available: http://www.idiap.ch/ resources/gestures4) Jing-Ming Guo, “Effective Hand Posture Recognition System with Hierarchical-Feature Adaboosting and Feature Reserving Average Mask” 2013/3/6 32
  33. 33. THANK YOU 2013/3/6 33

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