Skip to main content
SpringerLink
Log in

Gradient Local Auto-Correlations and Extreme Learning Machine for Depth-Based Activity Recognition

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

Included in the following conference series:

Abstract

This paper presents a new method for human activity recognition using depth sequences. Each depth sequence is represented by three depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues. A feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is introduced to extract features from DMMs. The gradient local auto-correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., histogram of oriented gradients) which use first order statistics (i.e., histograms). Based on the extreme learning machine, a fusion framework that incorporates feature-level fusion into decision-level fusion is proposed to effectively combine the GLAC features from DMMs. Experiments on the MSRAction3D and MSRGesture3D datasets demonstrate the effectiveness of the proposed activity recognition algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen, C., Jafari, R., Kehtarnavaz, N.: Improving human action recognition using fusion of depth camera and inertial sensors. IEEE Trans. Hum.-Mach. Syst. 45(1), 51–61 (2015)

    Article  Google Scholar 

  2. Chen, C., Liu, K., Jafari, R., Kehtarnavaz, N.: Home-based senior fitness test measurement system using collaborative inertial and depth sensors. In: EMBC, pp. 4135–4138 (2014)

    Google Scholar 

  3. Theodoridis, T., Agapitos, A., Hu, H., Lucas, S.M.: Ubiquitous robotics in physical human action recognition: a comparison between dynamic ANNs and GP. In: ICRA, pp. 3064–3069 (2008)

    Google Scholar 

  4. Chen, C., Kehtarnavaz, N., Jafari, R.: A medication adherence monitoring system for pill bottles based on a wearable inertial sensor. In: EMBC, pp. 4983–4986 (2014)

    Google Scholar 

  5. Yang, X., Tian, Y.: Super normal vector for activity recognition using depth sequences. In: CVPR, pp. 804–811 (2014)

    Google Scholar 

  6. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR, pp. 1297–1304 (2011)

    Google Scholar 

  7. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: CVPRW, pp. 9–14 (2010)

    Google Scholar 

  8. Yang, X., Zhang, C., Tian, Y.: Recognizing actions using depth motion maps-based histograms of oriented gradients. In: ACM Multimedia, pp. 1057–1060 (2012)

    Google Scholar 

  9. Xia, L., Aggarwal, J.K.: Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: CVPR, pp. 2834–2841 (2013)

    Google Scholar 

  10. Yang, X., Tian, Y.: Effective 3d action recognition using eigenjoints. J. Vis. Commun. Image Represent. 25(1), 2–11 (2014)

    Article  MathSciNet  Google Scholar 

  11. Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)

    Article  Google Scholar 

  12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)

    Google Scholar 

  13. Chen, C., Liu, K., Kehtarnavaz, N.: Real-time human action recognition based on depth motion maps. J. Real-Time Image Process., 1–9 (2013). doi:10.1007/s11554-013-0370-1

  14. Chen, C., Jafari, R., Kehtarnavaz, N.: Action recognition from depth sequences using depth motion maps-based local binary patterns. In: WACV, pp. 1092–1099 (2015)

    Google Scholar 

  15. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  16. Vemulapalli, R., Arrate, F., Chellappa, R.: Human action recognition by representing 3D skeletons as points in a lie group. In: CVPR, pp. 588–595 (2014)

    Google Scholar 

  17. Kobayashi, T., Otsu, N.: Image feature extraction using gradient local auto-correlations. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 346–358. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  18. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)

    Article  Google Scholar 

  19. Chen, C., Zhou, L., Guo, J., Li, W., Su, H., Guo, F.: Gabor-filtering-based completed local binary patterns for land-use scene classification. In: 2015 IEEE International Conference on Multimedia Big Data, pp. 324–329 (2015)

    Google Scholar 

  20. Li, W., Chen, C., Su, H., Du, Q.: Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 53(7), 3681–3693 (2015)

    Article  Google Scholar 

  21. Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classifiers 10(3), 61–74 (1999)

    Google Scholar 

  22. Kurakin, A., Zhang, Z., Liu, Z.: A real time system for dynamic hand gesture recognition with a depth sensor. In: EUSIPCO, pp. 1975–1979 (2012)

    Google Scholar 

  23. Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y.: Robust 3D action recognition with random occupancy patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 872–885. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  24. Vieira, A.W., Nascimento, E.R., Oliveira, G.L., Liu, Z., Campos, M.F.: STOP: space-time occupancy patterns for 3D action recognition from depth map sequences. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 252–259. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: CVPR, pp. 1290–1297 (2012)

    Google Scholar 

  26. Oreifej, O., Liu, Z.: HON4D: Histogram of oriented 4D normals for activity recognition from depth sequences. In: CVPR, pp. 716–723 (2013)

    Google Scholar 

  27. Rahmani, H., Mahmood, A., Huynh, D.Q., Mian, A.: Real time action recognition using histograms of depth gradients and random decision forests. In: WACV, pp. 626–633 (2014)

    Google Scholar 

  28. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)

    Article  Google Scholar 

Download references

Acknowledgement

We acknowledge the support of the Industry, Teaching and Research Prospective Project of Jiangsu Province (grant No. BY2015027-12), the Natural Science Foundation of China, under contracts 61063021, 61272052 and 61473086, and the Program for New Century Excellent Talents of the University of Ministry of Education of China.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, USA

    Chen Chen

  2. School of Information Science and Engineering, Changzhou University, Changzhou, China

    Zhenjie Hou

  3. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

    Baochang Zhang & Yun Yang

  4. School of Computer Science, China University of Geosciences, Wuhan, China

    Junjun Jiang

Authors
  1. Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenjie Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baochang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junjun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenjie Hou .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Chen, C., Hou, Z., Zhang, B., Jiang, J., Yang, Y. (2015). Gradient Local Auto-Correlations and Extreme Learning Machine for Depth-Based Activity Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27857-5_55

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation