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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 1

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Outline Introduction Related work Proposed method Experimental results Conclusion 2

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Introduction 3

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Problem: Can not separate a hand from a forearm using color and depth information Solution: Find wrist to recognize hand 4

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Related Work 5

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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] 6

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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,

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Related Work 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; Chuanyan Hu; Guanghui Yu; Changbo Wang, 2012 IEEE International Workshop on HAVE 8

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Proposed Method 9

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Hand segmentation and palm estimation Wrist recognition The center of the palm estimation 10

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

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

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

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Hand Segmentation and Palm Estimation (4/5) d. Detect the fingertips Find all candidate points that are both on the convex hull and the contour The distance of P 0 and P’> threshold 14

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

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

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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. 17

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

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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 19

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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. 20

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

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Estimating the Center of the Palm (1/4) Step 1: Select three points from the hand contour The three points (P 1, P 2, P 3 ) form an acute triangle. 22

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Estimating the Center of the Palm (2/4) Step 2: Find circumcenter O j of the triangle The O j coordinate The radius of circle : 23

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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 R ji > R j is bigger than the threshold Condition B: R j >minR (minR=> the minimum radius of the palm ) If A or B is not satisfied, Step 4 Estimating the Center of the Palm (3/4) 24

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

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

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Experimental Results 27

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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: m Minimum distance of line: 50 Minimum radius of the palm: 33 #hand contour inside circle: 25~50 28

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Experimental Results Before After 29

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Experimental Results Divide into three groups based on the number of the points inside the hand contour. #contour#inside contour a b c d e f g h i

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Experimental Results 31

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

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Conclusion 33

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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 34

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