Skip to main content
Log in

A target-centric surveillance system based on localization and social networking

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Surveillance systems are developed to enhance security and safety by constantly observing locations of interest. Although those systems can observe scenes of individual cameras separately, it is difficult to figure out what happened to the target moving across multiple cameras. This paper first proposes Video Diary Service (VDS) to solve this problem. VDS is an automatic video-oriented diary service, which keeps track of users’ lives. In addition, VDS can identify social networking relationships among the users, as well as record videos of the users. By exploiting these properties of VDS, we extend VDS into a new surveillance system called S-VDS. S-VDS is a target-centric surveillance system which focuses on the target, not the area, with its comprehensive information including the location, time, social relationship, and preferences. We then develop the basic functions of the proposed system and demonstrate its feasibility. We also illustrate three applications (i.e., a remote healthcare system, an anti-crime system, and a system for finding missing children), where the proposed system can enhance security and safety by considering individual surveillance purposes.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Apple ipad. http://www.apple.com/ipad/. Accessed 11 Nov 2012

  2. Apple iphone. http://www.apple.com/iphone/. Accessed 11 Nov 2012

  3. Becker JV, Kaplan MS, Cunningham-Rathner J, Kavoussi R (1986) Characteristics of adolescent incest sexual perpetrators: preliminary findings. J Fam Violence 1:85–97

    Article  Google Scholar 

  4. Best J (1990) Threatened children: rhetoric and concern about child-victims. University of Chicago Press

  5. Chen CD, Rao J (2010) The organization of mobile personal lifelog by activity. In: The Pacific-Rim conference on multimedia (PCM) (2010)

  6. Chen H, Wang FY (2005) Guest editors’ introduction: artificial intelligence for homeland security. IEEE Intell Syst 20:12–16

    Article  Google Scholar 

  7. Chen MY, Sohn T, Chmelev D, Haehnel D, Hightower J, Hughes J, Lamarca A, Potter F, Smith I, Varshavsky A (2006) Practical metropolitan-scale positioning for gsm phones. In: International conference on ubiquitous computing (UBICOMP)

  8. Cheng YC, Chawathe Y, LaMarca A, Krumm J (2005) Accuracy characterization for metropolitan-scale wi-fi localization. In: ACM the international conference on Mobile Systems, Applications, and Services (MOBISYS)

  9. Facebook. http://www.facebook.com. Accessed 11 Nov 2012

  10. Fong A, Hui S (2001) Web-based intelligent surveillance system for detection of criminal activities. Comput Control Eng J 12(6):263–270

    Article  Google Scholar 

  11. Han J, Lee J, Kwon TT, Jo D, Ha T, Choi Y (2010) How to mitigate signal dragging during wardriving. IEEE Pervasive Computing 9:20–27

    Article  Google Scholar 

  12. Hjelmas E, Low BK (2001) Face detection: a survey. Comput Vis Image Underst 83(3):236–274

    Article  MATH  Google Scholar 

  13. Hodges S, Williams L, Berry E, Izadi S, Srinivasan J, Butler A, Smyth G, Kapur N, Wood K (2006) Sensecam: a retrospective memory aid. In: International conference on Ubiquitous Computing (UBICOMP)

  14. Hossain S, Rahman A, El Saddik A (2011) Fusion of face networks through the surveillance of public spaces to address sociological security recommendations. In: IEEE International Conference on Multimedia and Expo (ICME)

  15. Hu W, Tan T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern, Part C Appl Rev 34(3):334–352

    Article  Google Scholar 

  16. Internet video diary. http://mmlab.snu.ac.kr/projects/2010/2010demo. Accessed 11 Nov 2012

  17. Lerner JS, Gonzalez RM, Small DA, Fischhoff B (2003) Effects of fear and anger on perceived risks of terrorism: a national field experiment. Psychol Sci 14(2):144–150

    Article  Google Scholar 

  18. Li L, Huang W, Gu IH, Luo R, Tian Q (2008) An efficient sequential approach to tracking multiple objects through crowds for real-time intelligent cctv systems. IEEE Trans Syst Man Cybern, Part B, Cybern 38(5):1254–1269

    Article  Google Scholar 

  19. Myspace. http://www.myspace.com. Accessed 11 Nov 2012

  20. National braodband paln by issues: health care. http://www.broadband.gov/issues/healthcare.html. Accessed 11 Nov 2012

  21. National braodband paln by issues: public safety and homeland security. http://www.broadband.gov/issues/public-safety.html. Accessed 11 Nov 2012

  22. Raty T (2010) Survey on contemporary remote surveillance systems for public safety. IEEE Trans Syst Man Cybern, Part C Appl Rev 40(5):493–515

    Article  Google Scholar 

  23. Saini M, Atrey P, Mehrotra S, Kankanhalli M (2011) Anonymous surveillance. In: IEEE International Conference on Multimedia and Expo (ICME), pp 1–6

  24. Saini M, Xiangyu W, Atrey P, Kankanhalli M (2011) Dynamic workload assignment in video surveillance systems. In: IEEE International Conference on Multimedia and Expo (ICME), pp 1–6

  25. Samsung Galaxy 2. http://www.samsung.com/global/microsite/galaxys2. Accessed 11 Nov 2012

  26. Silverlight. http://www.microsoft.com/silverlight. Accessed 11 Nov 2012

  27. Tseng YC, Wang YC, Cheng KY, Hsieh YY (2007) imouse: an integrated mobile surveillance and wireless sensor system. Computer 40(6):60–66

    Article  Google Scholar 

  28. Twitter. http://twitter.com. Accessed 11 Nov 2012

  29. Valera M, Velastin SA (2005) Intelligent distributed surveillance systems: a review. IEE Proc-Vis Image Signal Process 152(2):192–204

    Article  Google Scholar 

  30. Zhang Y, Partridge K, Reich J (2007) Localizing tags using mobile infrastructure. In: International symposium on location-and Context-Awareness (LoCA)

  31. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Jihoon Lee, Xiaofei Wang, Yongrok Kim, Mingu Cho, Wonyoung Kwak, Shinhaeng Oh, and Hyeseok Oh for their help in developing the proposed system and demonstrating its feasibility. This work was supported by the KCC (Korea Communications Commission), Korea, under the R&D program supervised by the KCA (Korea Communications Agency) (KCA-2012-11-911-05-002) and Seoul R&BD Program (WR080951) by Seoul Metropolitan Government. The ICT at Seoul National University provides research facilities.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jinyoung Han or Ted Taekyoung Kwon.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Han, J., Choi, N., Chung, T. et al. A target-centric surveillance system based on localization and social networking. Multimed Tools Appl 73, 241–265 (2014). https://doi.org/10.1007/s11042-012-1285-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1285-8

Keywords

Navigation