Advertisement

Active Histogram of Oriented Gradient Based Learning for Free Palm Tracking

  • Shuai ZhangEmail author
  • Xiang Chen
  • Kongqiao Wang
  • Jiangwei Li
  • Yanwei Pang
  • He Yan
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 133)

Abstract

Hand detection is a challenging research field in computer vision due tothe high freedom of hand for discrimination especially under low imaging conditions. In this paper, we mainly develop a novel feature that we called active Histogram of Oriented Gradient (aHOG) for palm detection inunconstrained grey-level images. Toovercome the limitationsof HOG, we apply local PCA, which is a feature synthesisprocedure, to original HOG feature sets. So that the output feature takesshorter description length and are less insensitive to light variations and background clusters, without much performance penalty. Then the features are combined with LBP for better palm information mining in linear SVM for classification. Besides, we use a scale partitionstrategy to achieve fast palm tracking. In our experiments, the performance is demonstrated to be very effective on the infrared palm database collected by ourselves, which involve rich inter-plane and out-of-plane rotations.

Keywords

palmtracking active histogram of oriented gradient feature synthesis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  2. 2.
    Lei, X., Jiangwei, L., Kongqiao, W.: Real-time and Multi-view Face Trackingon Mobile Platform. In: Acoustics, Speech and Signal Processing (ICASSP), pp. 1485–1488 (2011)Google Scholar
  3. 3.
    Rosales, R., Athitsos, V., Sigal, L., Sclaroff, S.: 3D Hand Pose Reconstruction using Specialized Mappings. In: Proc. Intl. Conf. Computer Vision, pp. 378–385 (2001)Google Scholar
  4. 4.
    Bretzner, L., Laptev, I., Lindeberg, T.: Hand Gesture Recognition using Multi-scale Color Features, Hierarchical Models and Particle Filtering. In: Proc. Intl. Conf. Face and Gesture Recognition, pp. 423–428 (2002)Google Scholar
  5. 5.
    Xiaoyu, W., Tony, X.H., Shuicheng, Y.: An HOG-LBP Human Detector with Partial Occlusion Handling. In: CVPR 2009, pp. 32–39 (2009)Google Scholar
  6. 6.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. In: PAMI 2002, pp. 971–987 (2002)Google Scholar
  7. 7.
    Fillbrandt, H., Akyol, S., Kraiss, K.F.: Extraction of 3D Hand Shape and Posture from Image Sequences for Sign Language Recognition. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, vol. 17, pp. 181–186 (October 2003)Google Scholar
  8. 8.
    Hamada, Y., Shimada, N., Shirai, Y.: Hand Shape Estimation under Complex Backgrounds for Sign Language Recognition. In: Proc. of 6th Int. Conf. on Automatic Face and Gesture Recognition, pp. 589–594 (2004)Google Scholar
  9. 9.
    Kolsch, M., Turk, M.: Robust Hand Detection. In: Proc. Intl. Conf. Face and Gesture Recognition, pp. 614–619 (2004)Google Scholar
  10. 10.
    Chang, C.C., Pengwu, C.M.: Gesture Recognition Approach for Sign Language using Curvature Scale Space and Hidden Markov Model. In: ICME, pp. 1187–1190 (2004)Google Scholar
  11. 11.
    Caglar, M.B., Lobo, N.: Open Hand Detection in a Cluttered Single Image using Finger Primitives. In: CVPRW 2006, p. 148 (2006)Google Scholar
  12. 12.
    Chen, Q., Georganas, N., Petriu, E.: Real-time Vision Based Hand Gesture Recognition using Haar-like Features. In: Instrumentation and Measurement Technology Conference Proceedings 2007, pp. 1–6 (2007)Google Scholar
  13. 13.
    Kawulok, M.: Dynamic Skin Detection in Color Images for Sign Language Recognition. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 112–119. Springer, Heidelberg (2008)Google Scholar
  14. 14.
    Yikai, F., Kongqiao, W., Jian, C., Hanqing, L.: A Real-Time Hand Gesture Recognition Method. In: International Conference on Multimedia and Expo. 2007, pp. 995–998 (2007)Google Scholar
  15. 15.
    Zhigeng, P., Yang, L., Mingmin, Z., Chao, S., Kangde, G., Xing, T., Zhou, S.Z.: A Real-time Multi-cue Hand Tracking Algorithm Based on Computer Vision. In: Virtual Reality Conference (VR 2010), pp. 219–222 (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Shuai Zhang
    • 1
    Email author
  • Xiang Chen
    • 1
  • Kongqiao Wang
    • 2
  • Jiangwei Li
    • 2
  • Yanwei Pang
    • 3
  • He Yan
    • 3
  1. 1.Department of Electrical Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Nokia Research CenterBeijingChina
  3. 3.Department of Electrical and Information EngineeringTianjin UniversityTianjinChina

Personalised recommendations