Skip to main content
SpringerLink
Log in

Analyzing Activities in Videos Using Latent Dirichlet Allocation and Granger Causality

  • 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

We propose an unsupervised method for analyzing motion activities from videos. Our method combines Latent Dirichlet Allocation with Granger Causality to discover the main motions composing the activity as well as to detect how these motions relate to one another in time and space. We tested our method on synthetic and real-world datasets. Our method compares favorably with state-of-the-art methods.

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. Li, J., Gong, S., Xiang, T.: Global behaviour inference using probabilistic latent semantic analysis. In: BMVC, vol. 3231, p. 3232 (2008)

    Google Scholar 

  2. Wang, X., Ma, X., Grimson, W.E.L.: Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Trans. Pattern Anal. Mach. Intell. 31(2009), 539–555 (2009)

    Article  Google Scholar 

  3. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  4. Guo, S., Seth, A.K., Kendrick, K.M., Zhou, C., Feng, J.: Partial Granger causality - eliminating exogenous inputs and latent variables. J. Neurosci. Meth. 172, 79–93 (2008)

    Article  Google Scholar 

  5. Saleemi, I., Shafique, K., Shah, M.: Probabilistic modeling of scene dynamics for applications in visual surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1472–1485 (2009)

    Article  Google Scholar 

  6. Kuettel, D., Breitenstein, M.D., Van Gool, L., Ferrari, V.: What’s going on? discovering spatio-temporal dependencies in dynamic scenes. In: IEEE CVPR, pp. 1951–1958 (2010)

    Google Scholar 

  7. Griffiths, T.: Gibbs sampling in the generative model of latent Dirichlet allocation. 518(11), 1–3 (2002). Standford University

    Google Scholar 

  8. Wiener, N.: The theory of prediction. In: Modern Mathematics for the Engineer, pp. 165–190. McGraw-Hill, New York (1956)

    Google Scholar 

  9. Granger, C.W.: Investigating causal relations by econometric models and cross-spectral methods. Econom. J. Econom. Soci. 33, 424–438 (1969)

    Google Scholar 

  10. Prabhakar, K., Oh, S., Wang, P., Abowd, G.D., Rehg, J.M.: Temporal causality for the analysis of visual events. In: IEEE CVPR, 1967–1974 (2010)

    Google Scholar 

  11. Fan, Y., Yang, H., Zheng, S., Su, H., Wu, S.: Video sensor-based complex scene analysis with Granger causality. Sensors 13, 13685–13707 (2013)

    Article  Google Scholar 

  12. Hospedales, T., Gong, S., Xiang, T.: Video behaviour mining using a dynamic topic model. Int. J. Comput. Vis. 98, 303–323 (2012)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Vision Laboratory, Department of Computer Sciences, Florida Institute of Technology, Melbourne, FL, USA

    Dalwinder Kular & Eraldo Ribeiro

Authors
  1. Dalwinder Kular
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eraldo Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dalwinder Kular .

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

Kular, D., Ribeiro, E. (2015). Analyzing Activities in Videos Using Latent Dirichlet Allocation and Granger Causality. 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_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27857-5_58

  • 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