Skip to main content
SpringerLink
Log in

Human Behavior Analysis at a Point of Sale

  • Conference paper
Advances in Visual Computing (ISVC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6455))

Included in the following conference series:

Abstract

This paper presents a method that analyzes human behavior in a shopping setting. Several actions are detected and we are especially interested in detecting interactions between customers and products. This paper first presents our application context, the advantages and constraint of a shopping setting. Then we present and evaluate several methods for human behavior understanding. Human actions are represented with Motion History Image (MHI), Accumulated Motion Image (AMI), Local Motion Context (LMC), and Interaction Context (IC). Then we use Support Vector Machines (SVM) to classify actions. Finally, we combine LMC and IC descriptors in a real-time system that recognizes human behaviors while shopping to enhance digital media impact at the point of sale.

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

Access this chapter

Log in via an institution
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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. ICCV 2, 1395–1402 (2005)

    Google Scholar 

  2. Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. on Pattern Analysis and Machine Intel. 23, 257–267 (2001)

    Article  Google Scholar 

  3. Chang, C., Lin, C.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

  4. Efros, A., Berg, A., Mori, G., Malik, J.: Recognizing action at a distance. In: ICCV (2003)

    Google Scholar 

  5. Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviours. IEEE Transac. on Syst., Man, and Cyb., 334–352 (2004)

    Google Scholar 

  6. Hu, Y., Cao, L., Lv, F., Yan, S., Gong, Y., Huang, T.S.: Action detection in complex scenes with spatial and temporal ambiguities. In: ICCV (2009)

    Google Scholar 

  7. Ikizler, N., Forsyth, D.: Searching video for complex activities with finite state models. In: CVPR (2007)

    Google Scholar 

  8. Kim, W., Lee, J., Kim, M., Oh, D., Kim, C.: Human action recognition using ordinal measure of accumulated motion. In: EURASIP JASP (2010)

    Google Scholar 

  9. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)

    Google Scholar 

  10. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: 7th IJCAI, pp. 674–679 (1981)

    Google Scholar 

  11. PETS: Performance Evaluation of Tracking and Surveillance, http://winterpets09.net/

  12. Poppe, R.: A survey on vision-based human action recognition. Im. & Vis. Comp. J. 28, 976–990 (2010)

    Article  Google Scholar 

  13. Rodriguez, M.D., Ahmed, J., Shah, M.: Action MACH: a spatio-temporal maximum average correlation height filter for action recognition. In: CVPR (2008)

    Google Scholar 

  14. Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: ICPR, vol. 3, pp. 32–36 (2004)

    Google Scholar 

  15. Sicre, R., Nicolas, H.: Shopping scenarios semantic analysis in videos. In: CBMI (2010)

    Google Scholar 

  16. Smeaton, A., Over, P., Kraaij, W.: Evaluation campaigns and TRECVid. In: ACM MIR (2006)

    Google Scholar 

  17. Tran, D., Sorokin, A.: Human activity recognition with metric learning. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 548–561. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  18. Turaga, P., Chellappa, R.: Machine recognition of human activities: a survey. IEEE Trans. on Circ. and Syst. for Video Tech. 18(11), 1473–1488 (2008)

    Article  Google Scholar 

  19. Weinland, D., Ronfard, R., Boyer, E.: Free view-point action recognition using motion history volumes. CVIU (104), 249–257 (2006)

    Google Scholar 

  20. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surveys (2006)

    Google Scholar 

  21. Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters 27(7) (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LaBRI, University of bordeaux, 351 Cours de la libération, 33405, Talence Cedex, France

    R. Sicre & H. Nicolas

  2. Mirane S.A.S, 16 rue du 18 mai 1945, 33150, Cenon, France

    R. Sicre & H. Nicolas

Authors
  1. R. Sicre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Nicolas
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Ronald Chung

  6. Dyna Vox Systems, Pittsburgh, PA, USA

    Riad Hammound

  7. King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

  8. Hewlett Packard Labs, Paolo Alto, CA, USA

    Tan Kar-Han

  9. The Ohio State University, Columbus, OH, USA

    Roger Crawfis

  10. Virtual Reality Lab, EPFL, Lausanne, Switzerland

    Daniel Thalmann

  11. NASA Ames Research Center, Clifton Park, NY, USA

    David Kao

  12. Kitware, Clifton Park, NY, USA

    Lisa Avila

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sicre, R., Nicolas, H. (2010). Human Behavior Analysis at a Point of Sale. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_65

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17277-9_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17276-2

  • Online ISBN: 978-3-642-17277-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation