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Wrist Recognition and the Center of the Palm Estimation Based on Depth Camera

Wrist Recognition and the Center of the Palm Estimation Based on Depth Camera. Zhengwei Yao ; Zhigeng Pan ; Shuchang Xu. Virtual Reality and Visualization (ICVRV), 2013 International Conference on. Outline. Introduction Related work Proposed method Experimental results Conclusion.

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Wrist Recognition and the Center of the Palm Estimation Based on Depth Camera

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  1. Wrist Recognition and the Center of the Palm Estimation Based on Depth Camera Zhengwei Yao ; Zhigeng Pan ; Shuchang Xu Virtual Reality and Visualization (ICVRV), 2013 International Conference on

  2. Outline • Introduction • Related work • Proposed method • Experimental results • Conclusion

  3. Introduction

  4. Introduction • Problem: Can not separate a hand from aforearm using color and depth information • Solution: Find wrist to recognize hand

  5. Related Work

  6. Related Work • Hand segmentation and extraction • Color [11,12] • Depth threshold [13,14] • The location of other body parts [15~17] • Wrist • Wear wristband[14] • Palm detection[18]

  7. Reference [13] D. Uebersax, J. Gall, and M. Van den Bergh, and L. Van Gool, “Realtime sign language letter and word recognition from depth data”. International Conference on Computer Vision Workshops (ICCV Workshops), 2011 [14] Z. Ren, J. Yuan, and Z. Zhang, “Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera”. ACM international conference on Multimedia, 2011 [15] T. I. Cerlinca and S. P. Pentiuc, “Robust 3D Hand Detection for Gestures Recognition”. Proc. the 5th International Symposium on Intelligent Distributed Computing, Delft, 2012 [16] M. Van den Bergh and L. Van Gool, “Combining RGB and ToF cameras for real-time 3D hand gesture interaction”. Workshop on Applications of Computer Vision (WACV), Kona, 2011 [17] K. Fujimura and L. Xia, “Sign recognition using depth image streams”. Automatic Face and Gesture Recognition, 2006 [18] U. Lee and J. Tanaka, “Hand Controller: Image Manipulation Interface Using Fingertips and Palm Tracking with Kinect Depth Data”. Proc. of 10th Asia Pacific Conference on Computer Human Interaction(APCHI), Matsue, 2012

  8. Related Work http://blog.candescent.ch/ http://candescentnui.codeplex.com/ • Candescent NUI(Natural User Interface) project • Hand and finger tracking • Develop by Stefan Stegmueller, Swiss • Open source: Use the OpenNI framework with the Kinect sensor “Finger direction detection” Blue: cluster centroid Green: palm center Red: fingertips Yellow: hand contour Long lines : finger directions ※“A Robust Method of Detecting Hand Gestures Using Depth Sensors”, Yan Wen; ChuanyanHu; GuanghuiYu; Changbo Wang, 2012 IEEE International Workshop on HAVEhttp://www.camdemy.com/media/11513

  9. Proposed Method

  10. Proposed Method • Hand segmentation and palmestimation • Wrist recognition • The center of the palm estimation

  11. Hand Segmentation and PalmEstimation (1/5) • a. Cluster the hand data • K-means clustering algorithm • Specify the depth range: 0.5~0.8m

  12. Hand Segmentation and PalmEstimation (2/5) • b. Compute the Convex hull of the hands • The Graham scan algorithm

  13. Hand Segmentation and PalmEstimation (3/5) • c. Detect the hand contours • Moor-Neighbor tracking algorithm

  14. Hand Segmentation and PalmEstimation (4/5) • d. Detect the fingertips • Find all candidate points that are both on the convex hull and the contour • The distance of P0 and P’> threshold

  15. Hand Segmentation and PalmEstimation (5/5) • e. Estimate the center of the palm • The biggest circle inside the hand contour

  16. Wrist Recognition • Wrist: pitpoints • Find an obvious pit point in the contour of hand • Create an appropriate to find another wrist point. inscribed rectangle

  17. Wrist Recognition (1/4) • Step 1: Find candidate lines of wrist • The ends of the candidate line should not be both fingertips. • The distance of the candidate line should not be less than a specific value.

  18. Wrist Recognition (2/4) • Step 2: Find the corresponding candidate contours whose ends are the ends of the candidate lines.

  19. Wrist Recognition (3/4) • Step 3: Find one of the wrist points • Calculate the maximum distance between the candidate line and the corresponding candidate contour. • The largest distance from these maximum distances. • The point with the largest distance is one of the wrist points

  20. Wrist Recognition (4/4) • Step 4: Find another wrist point • Connect this wrist point to each point in the hand contour, and take these connecting lines as the diagonals of rectangles. • If the rectangle is not inside the hand contour, the corresponding point in the contour is not another wrist point. • Find out the point with the shortest rectangle diagonal as another wrist point.

  21. Wrist Recognition • Candidate lines • Corresponding contour • Find one of the wrist points • Find another wrist point

  22. Estimating the Center of the Palm (1/4) • Step 1: Select three points from the hand contour • The three points (P1, P2, P3)form an acute triangle.

  23. Estimating the Center of the Palm (2/4) • Step 2: Find circumcenterOj of the triangle • The Oj coordinate • The radius of circle :

  24. Estimating the Center of the Palm (3/4) • Step 3: Determine the center • Calculate the distances from each point in the hand contour to the center Formula. • Condition A: The number of distance Rji >Rjis bigger than the threshold • Condition B: Rj>minR (minR=> the minimum radius of the palm ) • If A or B is not satisfied, Step 4

  25. Estimating the Center of the Palm (3/4) • Step 4: Find another appropriate palm center • One end-point of these two intersectant chords is replaced by point Pmin • Repeat step 2 to step 4 until the ending condition is true

  26. Proposed Method Fingertips detection • Hand segmentation and palmestimation • Wrist recognition • The center of the palm estimation

  27. Experimental Results

  28. Experimental Results • Device: AMD Athlon(tm)Formula Dual Core Processor Formula CPU, 4GB RAM, NVIDIA GeForce 9600GT Graphics card and Window7 32bit OS • Threshold setting • Depth: 0.5-0.8m • Minimum distance of line: 50 • Minimum radius of the palm: 33 • #hand contour inside circle: 25~50

  29. Experimental Results Before After

  30. Experimental Results • Divide into three groups based on the number of the points inside the hand contour.

  31. Experimental Results

  32. Experimental Results • Improved original algorithm: every 8th point • The new algorithm: proposed method

  33. Conclusion

  34. Conclusion • Propose the wrist recognition algorithm to separate the hand from the forearm, • Propose a new algorithm of estimating the center of the palm to reduce the computing time. • Without Kinect SDK

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