Hand Pose Estimation from Local Surface Normals

We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals. The hierarchical regression follows the tree structured topology of hand from wrist to finger tips. We propose a conditional regression forest, i.e. the Frame Conditioned Regression Forest (FCRF) which uses a new normal difference feature. At each stage of the regression, the frame of reference is established from either the local surface normal or previously estimated hand joints. By making the regression with respect to the local frame, the pose estimation is more robust to rigid transformations. We also introduce a new efficient approximation to estimate surface normals. We verify the effectiveness of our method by conducting experiments on two challenging real-world datasets and show consistent improvements over previous discriminative pose estimation methods.

[1]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[2]  Tae-Kyun Kim,et al.  Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Li Cheng,et al.  Efficient Hand Pose Estimation from a Single Depth Image , 2013, 2013 IEEE International Conference on Computer Vision.

[4]  Andrew W. Fitzgibbon,et al.  Accurate, Robust, and Flexible Real-time Hand Tracking , 2015, CHI.

[5]  Vincent Lepetit,et al.  Training a Feedback Loop for Hand Pose Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[6]  Tae-Kyun Kim,et al.  Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Antonis A. Argyros,et al.  Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties , 2015, BMVC.

[8]  Ken Perlin,et al.  Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks , 2014, ACM Trans. Graph..

[9]  Daniel Thalmann,et al.  Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization , 2013, The Visual Computer.

[10]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Antti Oulasvirta,et al.  Fast and robust hand tracking using detection-guided optimization , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Manolis I. A. Lourakis,et al.  Evolutionary Quasi-Random Search for Hand Articulations Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Lale Akarun,et al.  Hand Pose Estimation and Hand Shape Classification Using Multi-layered Randomized Decision Forests , 2012, ECCV.

[14]  Andrew W. Fitzgibbon,et al.  The Vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Ronen Basri,et al.  Learning 3D Articulation and Deformation using 2D Images , 2015, ArXiv.

[16]  Mircea Nicolescu,et al.  Vision-based hand pose estimation: A review , 2007, Comput. Vis. Image Underst..

[17]  Björn Stenger,et al.  Model-based hand tracking using a hierarchical Bayesian filter , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Yi Yang,et al.  Depth-Based Hand Pose Estimation: Data, Methods, and Challenges , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  Jian Sun,et al.  Cascaded hand pose regression , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Vincent Lepetit,et al.  Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes , 2011, 2011 International Conference on Computer Vision.

[21]  Nico Blodow,et al.  Aligning point cloud views using persistent feature histograms , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Antonis A. Argyros,et al.  Efficient model-based 3D tracking of hand articulations using Kinect , 2011, BMVC.

[23]  Luc Van Gool,et al.  Real-time facial feature detection using conditional regression forests , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Jitendra Malik,et al.  Learning Rich Features from RGB-D Images for Object Detection and Segmentation , 2014, ECCV.

[25]  Haibin Ling,et al.  3D Hand Pose Estimation Using Randomized Decision Forest with Segmentation Index Points , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[26]  Luc Van Gool,et al.  Motion Capture of Hands in Action Using Discriminative Salient Points , 2012, ECCV.

[27]  Karthik Ramani,et al.  A Collaborative Filtering Approach to Real-Time Hand Pose Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[28]  Tae-Kyun Kim,et al.  Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests , 2013, 2013 IEEE International Conference on Computer Vision.

[29]  Chen Qian,et al.  Realtime and Robust Hand Tracking from Depth , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Konrad Schindler,et al.  A Generalisation of the ICP Algorithm for Articulated Bodies , 2008, BMVC.

[31]  Andrew Blake,et al.  Efficient Human Pose Estimation from Single Depth Images , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.