3D Fingertip and Palm Tracking in Depth Image Sequences Hui Liang, Junsong Yuan and Daniel Thalmann Proceedings of the 20th ACM international conference on Multimedia 2012

Outline Introduction Related Work Proposed Method Experimental Results Conclusion

Introduction

Introduction Human hand is an essential body part for human-computer interaction. The positions of tracked fingertips: gesture estimation Difficulty in fingertip tracking: Anaglyph 紅藍眼鏡 Polarization 偏光眼鏡 – 在一般LCD TV前面貼上一層微相位差膜(Micro-retarder)，利用光的偏振方向 來將左眼與右眼的影像分離 Shutter 快門眼鏡 - 更新頻率120Hz以上播放左、右眼視角畫面，藉由快速切換左右眼資訊，使得左右眼分別看到正確的左眼與右眼畫面，經過視覺暫留與大腦融合後，即可呈現出具 深度感的立體影像。 此技術所顯示的3D畫面解析度不會下降，且立體效果非常的好。 然而由於要提升3D影像品質，因此左眼與右眼觀看螢幕的時間非常短，使得整體亮度會下降許多，亦是目前研發上需克服的重點。 Side-by-side Bending Nearby

Introduction Many previous methods: In this paper: Only focus on extracting 2D fingertips Cannot track fingertips robustly for a freely moving hand In this paper: Present a robust fingertip and palm tracking scheme With the input of depth images (KINECT) Track the 3D fingertip positions quite accurately Anaglyph 紅藍眼鏡 Polarization 偏光眼鏡 – 在一般LCD TV前面貼上一層微相位差膜(Micro-retarder)，利用光的偏振方向 來將左眼與右眼的影像分離 Shutter 快門眼鏡 - 更新頻率120Hz以上播放左、右眼視角畫面，藉由快速切換左右眼資訊，使得左右眼分別看到正確的左眼與右眼畫面，經過視覺暫留與大腦融合後，即可呈現出具 深度感的立體影像。 此技術所顯示的3D畫面解析度不會下降，且立體效果非常的好。 然而由於要提升3D影像品質，因此左眼與右眼觀看螢幕的時間非常短，使得整體亮度會下降許多，亦是目前研發上需克服的重點。

Related Work

Related work Focus only on 2D fingertips:[4][5][6][9] Based on contour analysis of the extracted hand region:[2][4][5][6] Usually can track the fingertips for only stretched fingers.

Related work In [6], Fingertips are tracked for infrared image sequences. It utilizes a template matching strategy Fingertip tracking : Kalman filter In [2], Stereoscopic vision is adopted Maximize the distance center of gravity of the hand & the boundary

Related work In [9] (Kinect), Minimum depth Depth < Threshold [9] J. L. Raheja, A. Chaudhary, and K. Singal. Tracking of fingertips and centres of palm using kinect, 2011. Proc. Of the 3rd Int’l Conf. on Computational Intelligence, Modelling and Simulation. Related work In [9] (Kinect), Minimum depth Depth < Threshold Circular filter

Related work [2] S. Consei1, S. Bourennane, and L. Martin. Three dimensional fingertip tracking in stereovision, 2005. Proc. of the 7th Int’l Conf. on Advanced Concepts for Intelligent Vision Systems. [4] K. Hsiao, T. Chen, and S. Chien. Fast fingertip positioning by combining particle filtering with particle random diffusion, 2008. Proc. IEEE Int’l Conf. on Multimedia and Expo. [5] I. Katz, K. Gabayan, and H. Aghajan. A multi-touch surface using multiple cameras, 2007. Proc. of the 9th Int’l Conf. on Advanced concepts for intelligent vision systems. [6] K. Oka, Y. Sato, and H. Koike. Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems, 2002. Proc. IEEE Int’l Conf. on Automatic Face and Gesture Recognition. [9] J. L. Raheja, A. Chaudhary, and K. Singal. Tracking of fingertips and centres of palm using kinect, 2011. Proc. Of the 3rd Int’l Conf. on Computational Intelligence, Modelling and Simulation.

Proposed Method

Foreground Segmentation Overview Stereo images (Depth map) Foreground Segmentation Palm Localization Hand Segmentation Fingertip Detection Fingertip Tracking Fingertips

Hand and Palm Detection 1) Assume the hand is the nearest object 2) Constrain global hand rotation by: : global rotation angle of the hand Foreground Segmentation Palm Localization Hand

Hand and Palm Detection Foreground Segmentation  Palm Localization Threshold the depth frame to obtain the foreground F : p : a pixel coordinate z(p) : depth value (of point p ) z0 : the minimum depth value zD : depth threshold Hand Segmentation 0.2m foreground F

Hand and Palm Detection Foreground Segmentation Palm Localization The palm region is approximated with a circle: pp : the palm center (of point p ) rp : the radius Assume that hand palm forms a globally largest blob Cp equals to the largest inscribed circle of the contour of F . 2D Kalman filter : reduce computational complexity Hand Segmentation

Hand and Palm Detection Foreground Segmentation Palm Localization Separate hand and forearm by a line: 1) Tangent to Cp 2) Perpendicular to the orientation of the forearm Orientation of the forearm : The Eigenvector that corresponds to the largest Eigenvalue of the covariance matrix of the contour pixel coordinates of F Hand Segmentation Hand region : FV(2D) → FD(3D)

Fingertip Detection and Tracking Constraints on possible fingertip locations: 1) Only in depth discontinuous region ( in contour Fv) 2) | Depth(one point) – Depth(neighborhoods) | are important. 3) Utilize the 3D geodesic shortest path (GSP) Fingertip vs. Non-fingertip Nearby Fingertips Fingertip Detection Tracking

Fingertip Detection Fingertip detection Fingertip tracking Goal: detect all five fingertips in the depth image Based on three depth-based features Build a graph G : Vh : contains of all points within FV (hand contour) Eh : for each pair of vertices(p,q), 1) they are in the 8-neighborhood of each other 2) their 3D distance 𝑑 𝑝,𝑞 = 𝑝−𝑞 2 is within threshold τ Edge weight : 3D Euclidean distance

Fingertip Detection Calculate Geodesic distance dg(p): Fingertip tracking Calculate Geodesic distance dg(p): From palm center pp for each vertex ∈ Vh Dijkstra graph search on Gh GSP point set Ug(p): The set of vertices on the shortest path from pp to p Rectangle local feature RL(p): Describe the neighborhood of a point p in FV 𝜂(𝑝) : ratio of 1s 1 X 1cm

Fingertip Detection Calculate Geodesic distance dg(p): 𝜂(𝑝) dg(p) Fingertip tracking Calculate Geodesic distance dg(p): 𝜂(𝑝) 0.4 dg(p) fingertips

Fingertip Detection Fingertip labeling: Fingertip tracking Fingertip labeling: Nmax=6 j : label 𝚪 1 2 3 4 5 𝜞(𝒊,𝒋) :estimate the probability that 𝑝 𝑓 𝑖 has the label lj 𝑵 : number of GSP points 𝒌 : kth GSP point i : fingertip

Fingertip Detection Fingertip labeling: Fingertip detection Fingertip tracking Fingertip labeling:

Fingertip Tracking Build a particle filter for each fingertip Fingertip detection Fingertip tracking Build a particle filter for each fingertip (x, ω) denote a particle x : 2D position in FV ω : the particle weight Constrain the positions of each particle to the border point set UB to reduce the search space

Fingertip Tracking Likelihood function : / / pre now 𝑹𝑳(𝒑) Metric Fingertip detection Fingertip tracking Likelihood function : pre now 𝑹𝑳(𝒑) Metric parameters difference Hausdorff difference feature difference Geodesic distance / dg(p) GSP points Neighbor depth /

Fingertip Tracking Likelihood function : Fingertip detection

Experimental Results

Experimental Results Quantitative results on synthetic sequences: ‧Error : Euclidean distance ‧Ground truth : phalanx end point Quantitative results on synthetic sequences: Seq. No. Motion Seq. 1 grasping Seq. 4 flexion Seq. 2 adduction/abduction Seq. 5 global rotation Seq. 3 successive single finger Seq. 6 combination of grasping and global rotation

Experimental Results

Experimental Results Virtual object grasping:

Conclusion

Conclusion Using multiple depth-based features for accurate fingertip localization Adopting a particle filter to track the fingertips over successive frames Track the 3D positions of fingertips robustly Great potential for extension to other HCI applications