An Incremental PCA-HOG Descriptor for Robust Visual Hand Tracking

Hand tracking in complicate scenarios is a crucial step to any hand gesture recognition systems. In this paper, we present a novel hand tracking algorithm with adaptive hand appearance modeling. In the algorithm, the hand image is first transformed to the grids of Histograms of Oriented Gradients. And then an incremental Principle Component Analysis is implemented. We name this operator an incremental PCA-HOG (IPHOG) descriptor. The exploitation of this descriptor helps the tracker dealing with vast changing of hand appearances as well as clutter background. Moreover, Particle filter method with certain improvements is also introduced to establish a tracking framework. The experimental results are conducted on an indoor scene with clutter and dynamic background. And the results are also compared with some traditional tracking algorithms to show its strong robustness and higher tracking accuracy.

[1]  Bernd Neumann,et al.  Computer Vision — ECCV’98 , 1998, Lecture Notes in Computer Science.

[2]  Horst Bischof,et al.  On-line Boosting and Vision , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Yi-Ping Hung,et al.  Appearance-guided particle filtering for articulated hand tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Takio Kurita,et al.  Selection of Histograms of Oriented Gradients Features for Pedestrian Detection , 2007, ICONIP.

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

[6]  Kyoung Mu Lee,et al.  Visual tracking via geometric particle filtering on the affine group with optimal importance functions , 2009, CVPR.

[7]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[8]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Zhan Song,et al.  An improved unscented particle filter for visual hand tracking , 2010, 2010 3rd International Congress on Image and Signal Processing.

[10]  Min C. Shin,et al.  Objective evaluation of approaches of skin detection using ROC analysis , 2007, Comput. Vis. Image Underst..

[11]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[12]  Michael I. Mandel,et al.  Visual Hand Tracking Using Nonparametric Belief Propagation , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[13]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[14]  Paulo Menezes,et al.  Human-robot interaction based on Haar-like features and eigenfaces , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[15]  Shan Lu,et al.  Using multiple cues for hand tracking and model refinement , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[16]  James J. Little,et al.  Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[17]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, ECCV.

[18]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.