Skip to main content
SpringerLink
Log in

World Expo Problem and Its Mixed Integer Programming Based Solution

  • Conference paper
Behavior and Social Computing (BSIC 2013, BSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8178))

Abstract

In this paper, we introduce an interesting “World Expo problem”, which aims to identify and track multiple targets in a sensor network, and propose a solution to this problem based on the mixed integer programming. Compared with traditional tracking problem in the sensor network, the World Expo problem has following two features. Firstly, the target in the network is not limited to single individuals. It can also be a group composed of multiple individuals with same path in the network, which implies that multiple targets can share the same path and be detected by the same sensor at the same time. Moreover, both the size and the number of groups are unknown. Secondly, differing from traditional sensor networks, the sensor network in the World Expo problem usually is sparse. These two features increase the difficulty in identification and tracking. To solve the aforementioned problem, we analyze the solvability of this problem and come up with a mixed integer programming based algorithm. The simulation result shows that our method has good performances and is robust to errors in the data.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Han, M., Xu, W., Tao, H., Gong, Y.: An algorithm for multiple object trajectory tracking. In: CVPR, pp. 864–871 (2004)

    Google Scholar 

  2. Saisan, P., Medasani, S., Owechko, Y.: Multi-view classifier swarms for pedestrian detection and tracking. In: CVPR, pp. 18 (2005)

    Google Scholar 

  3. Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 1–14. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Leibe, B., Schindler, K., Cornelis, N., Van Gool, L.: Coupled object detection and tracking from static cameras and moving vehicles. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1683–1698 (2008)

    Article  Google Scholar 

  5. Pellegrini, S., Ess, A., Schindler, K., van Gool, L.: You’ll never walk alone: Modeling social behavior for multi-target tracking. In: ICCV, pp. 261–268 (2009)

    Google Scholar 

  6. Eagle, N., Pentland, A.: Reality mining: Sensing complex social system. Personal and Ubiquitous Computing 10(4), 255–268 (2006)

    Article  Google Scholar 

  7. Celikoglu, H.B., Cigizoglu, H.K.: Public transportation trip flow modeling with generalized regression neural networks. Advances in Engineering Software 38, 71–79 (2007)

    Article  Google Scholar 

  8. Vinciarelli, A., Pantic, M., Bourlard, H., Pentland, A.: Social signal processing: State-of-the-art and future perspectives of an emerging domain. In: Proceedings of the ACM International Conference on Multimedia, Vancouver, Canada, pp. 1061–1070 (2008)

    Google Scholar 

  9. Cristani, M., Murino, V., Vinciarelli, A.: Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space. In: Proceedings of International Workshop on Society Intelligent Surveillance and Monitoring, San Francisco, USA, pp. 51–58 (2010)

    Google Scholar 

  10. Pickard, G., Pan, W., et al.: Time-critical social mobilization. Science 334(6055), 509–512 (2011)

    Article  Google Scholar 

  11. Olfati-Saber, R.: Distributed kalman filtering for sensor networks. In: 46th IEEE Conference on Decision and Control, pp. 5492–5498 (2007)

    Google Scholar 

  12. Djuric, P., Vemula, M., Bugallo, M.: Target tracking by particle filtering in binary sensor networks. IEEE Transactions on Signal Processing 56(6), 2229–2238 (2008)

    Article  MathSciNet  Google Scholar 

  13. Oh, S., Sastry, S.: Tracking on a graph. In: Fourth International Symposium on Information Processing in Sensor Networks (IPSN), pp. 195–202 (2005)

    Google Scholar 

  14. De, D., Song, W.Z., Xu, M., Cook, D., Huo, X.: Real-time tracking of motion trajectories from anonymous binary sensing in smart environments. In: The 32nd International Conference on Distributed Computing Systems (2012)

    Google Scholar 

  15. Shrivastava, N., Madhow, R.M.U., Suri, S.: Target tracking with binary proximity sensors: fundamental limits, minimal descriptions, and algorithms. In: 4th International Conference on Embedded Networked Sensor Systems, pp. 251–264 (2006)

    Google Scholar 

  16. Crespi, V., Cybenko, G., Jiang, G.: The theory of trackability with applications to sensor networks. ACM Transactions on Sensor Networks (TOSN) 4(3), 16 (2008)

    Google Scholar 

  17. Shrivastava, N., Mudumbai, R., Madhow, U., Suri, S.: Target tracking with binary proximity sensors. ACM Transactions on Sensor Networks (TOSN) 5(4), 30 (2009)

    Google Scholar 

  18. Lazos, L., Poovendran, R., Ritcey, J.: Analytic evaluation of target detection in heterogeneous wireless sensor networks. ACM Transactions on Sensor Networks (TOSN) 5(2), 18 (2009)

    Google Scholar 

  19. Arora, A., Ramnath, R., et al.: Exscal: elements of an extreme scale wireless sensor network. In: 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 102–108 (2005)

    Google Scholar 

  20. Arora, A., Dutta, P., et al.: A line in the sand: a wireless sensor network for target detection, classification, and tracking. Computer Networks 46(5), 605–634 (2004)

    Article  Google Scholar 

  21. Singh, J., Madhow, U., Kumar, R., Suri, S., Cagley, R.: Tracking multiple targets using binary proximity sensors. In: 6th International Conference on Information Processing in Sensor Networks, pp. 529–538 (2007)

    Google Scholar 

  22. Wang, C., Huo, X., Song, W.Z.: An integer programming approach for multiple-target trajectory identification with binary proximity sensors. Annals of Operations Research (2012) (submitted)

    Google Scholar 

  23. Busnel, Y., Querzoni, L., Baldoni, R., Bertier, M., Kermarrec, A.: Analysis of deterministic tracking of multiple objects using a binary sensor network. ACM Transactions on Sensor Networks (TOSN) 42(4), 8 (2011)

    Google Scholar 

  24. Cao, L.: In-depth behavior understanding and use: the behavior informatics approach. Information Sciences 180(17), 3067–3085 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Image Communication, Shanghai Jiao Tong University, Shanghai, China

    Hongteng Xu, Dixin Luo & Xiaokang Yang

  2. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, U.S.A.

    Xiaoming Huo

Authors
  1. Hongteng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dixin Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoming Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaokang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Advanced Analytics Institute, University of Technology, 2-12 Blackfriars Street, Chippendale, Blackfriars Campus, NSW 2008, Sydney, Australia

    Longbing Cao

  2. Institute of Scientific and Industrial Research, Osaka University, Japan

    Hiroshi Motoda

  3. Department of Computer Science, University of Minnesota, USA

    Jaideep Srivastava

  4. School of Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore

    Ee-Peng Lim

  5. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  6. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., Rm 1138 SEO, 60607, Chicago, IL, USA

    Philip S. Yu

  7. Leibniz Universität Hannover, Germany

    Wolfgang Nejdl

  8. Advanced Analytics Institute, University of Technology, Sydney, Australia

    Guandong Xu

  9. Deakin University, Burwood, VIC, Australia

    Gang Li

  10. Shanghai Jiao Tong University, 200240, Shanghai, China

    Ya Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Xu, H., Luo, D., Huo, X., Yang, X. (2013). World Expo Problem and Its Mixed Integer Programming Based Solution. In: Cao, L., et al. Behavior and Social Computing. BSIC BSI 2013 2013. Lecture Notes in Computer Science(), vol 8178. Springer, Cham. https://doi.org/10.1007/978-3-319-04048-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-04048-6_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04047-9

  • Online ISBN: 978-3-319-04048-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation