Information Systems Frontiers

, Volume 14, Issue 3, pp 499–515 | Cite as

Effective distributed service architecture for ubiquitous video surveillance

  • Ray-I Chang
  • Te-Chih Wang
  • Chia-Hui Wang
  • Jen-Chang Liu
  • Jan-Ming Ho
Article

Abstract

Video surveillance systems are playing an important role to protect lives and assets of individuals, enterprises and governments. Due to the prevalence of wired and wireless access to Internet, it would be a trend to integrate present isolated video surveillance systems by applying distributed computing environment and to further gestate diversified multimedia intelligent surveillance (MIS) applications in ubiquity. In this paper, we propose a distributed and secure architecture for ubiquitous video surveillance (UVS) services over Internet and error-prone wireless networks with scalability, ubiquity and privacy. As cloud computing, users consume UVS related resources as a service and do not need to own the physical infrastructure, platform, or software. To protect the service privacy, preserve the service scalability and provide reliable UVS video streaming for end users, we apply the AES security mechanism, multicast overlay network and forward error correction (FEC), respectively. Different value-added services can be created and added to this architecture without introducing much traffic load and degrading service quality. Besides, we construct an experimental test-bed for UVS system with three kinds of services to detect fire and fall-incident features and record the captured video at the same time. Experimental results showed that the proposed distributed service architecture is effective and numbers of services on different multicast islands were successfully connected without influencing the playback quality. The average sending rate and the receiving rates of these services are quite similar, and the surveillance video is smoothly played.

Keywords

Ubiquitous video surveillance Multimedia intelligent surveillance Multicast overlay network Forward error correction AES security mechanism Cloud computing 

References

  1. Advanced Encryption Standard, Federal Information Processing Standard 197. (2001). NIST (National Institute of Standards and Technology).Google Scholar
  2. Beynon, M. D., VanHook, D. J., Seibert, M., Peacock, A. & Dudgeon, D. (2003). Detecting abandoned packages in a multi-camera video surveillance system. In: IEEE Conference on Advanced Video and Signal Based Surveillance, 221–228.Google Scholar
  3. Bogaert, M., Chleq, N., Cornez, P., Regazzoni, C., Teschioni, S. A., & Thonnat, M. (1996). The PASSWORDS project. In: IEEE International Conference on Image Processing, 675–678.Google Scholar
  4. Bramberger, M., Doblander, A., Maier, A., Rinner, B., & Schwabach, H. (2006). Distributed embedded smart cameras for surveillance applications. Computer, 39, 68–75. doi:10.1109/MC.2006.55.CrossRefGoogle Scholar
  5. Celik, T., Demirel, H., Ozkaramanli, H., & Uyguroglu, M. (2007). Fire detection using statistical color model in video sequences. Journal of Visual Communication and Image Representation, 18, 176–185. doi:10.1016/j.jvcir.2006.12.003.CrossRefGoogle Scholar
  6. Chen, C. S., Wang, C. H., Shin, H. Y., & Hsu, W. H. (2009). Load-sharing overlay network design for ubiquitous video surveillance services. In: International Workshop on Peer-To-Peer Networking (P2PNet'09) of International Conference on Ultra Modern Telecommunications (ICUMT 2009), 12–14.Google Scholar
  7. Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., et al. (2000). A system for video surveillance and monitoring. VSAM Final Report, Robotics Institute, Carnegie Mellon University. Technical report CMU-RI-TR-00-12.Google Scholar
  8. Connell, J., Senior, A. W., Hampapur, A., Tian, Y. L., Brown, L., & Pankanti, S. (2004). Detection and tracking in the IBM peoplevision system. In: IEEE ICME, 1403–1406.Google Scholar
  9. Deering, S. (1989). Host extensions for IP multicasting. IETF RFC 1112.Google Scholar
  10. Dogan, S., Sadka, A. H., & Kondoz, A. M. (2001). MPEG-4 video transcoder for mobile multi-media traffic planning. In: IEE International Conference on 3G Mobile Communication Technologies, 109–113.Google Scholar
  11. Foresti, G. L., & Regazzoni, C. S. (1995). Localization and tracking of multiple unknown objects in real environments. Electronics Letters, 31, 355–356.CrossRefGoogle Scholar
  12. Foresti, G. L., Marcenaro, L., & Regazzoni, C. S. (2002). Automatic detection and indexing of video-event shots for surveillance applications. IEEE Transactions on Multimedia, 4, 459–471. doi:10.1109/TMM.2002.802024.CrossRefGoogle Scholar
  13. Goshorn, R., Goshorn, J., Goshorn, D., & Aghajan, H. (2007). Architecture for cluster-based automated surveillance network for detecting and tracking multiple persons. In: 1st ACM/IEEE International Conference on Distributed Smart Cameras, 219-226.Google Scholar
  14. Hanzo, L., Cherriman, P., & Kuan, E. (2000). Interactive cellular and cordless video telephony: State-of-the-art system design principles and expected performances. Proceedings of the IEEE, 88, 1388–1413. doi:10.1109/5.883314.CrossRefGoogle Scholar
  15. Juang, C. F., & Chang, C. M. (2007). Human body posture classification by a neural fuzzy network and home care system application. IEEE Transactions on Systems, Man and Cybernetics, 37, 984–994. doi:10.1109/TSMCA.2007.897609.CrossRefGoogle Scholar
  16. Kandhalu, A., Rowe, A., Huang, R. R. C., & Yeh, C. C. (2009). Real-time video surveillance over IEEE 802.11 Mesh Networks. In: Proceedings of the 15th IEEE Real-Time and Embedded Technology and Applications Symposium, 205-214.Google Scholar
  17. Keller, T., & Hanzo, L. (2000). Adaptive multicarrier modulation: a convenient framework for time-frequency processing in wireless communications. Proceedings of the IEEE, 88, 611–640. doi:10.1109/5.849157.CrossRefGoogle Scholar
  18. Kim, T., & Ammar, M. H. (2005). A comparison of heterogeneous video multicast schemes: layered encoding or stream replication. IEEE Transactions on Multimedia, 7, 1123–1130. doi:10.1109/TMM.2005.858376.CrossRefGoogle Scholar
  19. Kumar, V. (Ed.). (1995). Mbone interactive multimedia on the internet. New Readers.Google Scholar
  20. Liu, C. B. & Ahuja, N. (2004). Vision based fire detection. In: Proceedings of the 17th International Conference on Pattern Recognition, 134-137.Google Scholar
  21. Luby, M., Fountain, D., Vicisano, L., Cisco, Gemmell, J., et al. (2002). Forward error correction (FEC) building block. IETF RFC 3452.Google Scholar
  22. Macker, J. P. (1997). Reliable Multicast Transport and Integrated Erasure-based Forward Error Correction. In: Proceedings of the IEEE MILCOM, 973-977.Google Scholar
  23. Marcenaro, L., Oberti, F., Foresti, G. L., & Regazzoni, C. S. (2001). Distributed architectures and logical-task decomposition in multimedia systems. Proceedings of the IEEE, 89, 1419–1440. doi:10.1109/5.959339.CrossRefGoogle Scholar
  24. Phillips, W., III, Shah, M., & Lobo, N. (2002). Flame recognition in video. Pattern Recognition Letters, 23, 319–327. doi:10.1016/S0167-8655(01)00135-0.CrossRefGoogle Scholar
  25. Quinn, B. (2001). IP multicast applications: challenges and solutions. IETF RFC 3170.Google Scholar
  26. Regazzoni, C. S., & Tesei, A. (1996). Distributed data fusion for real-time crowding estimation. Signal Processing, 53, 47–63. doi:10.1016/0165-1684(96)00075-8.CrossRefGoogle Scholar
  27. Regazzoni, C. S., & Sacchi, C. (2000). A distributed surveillance system for detection of abandoned objects in unmanned railway environments. IEEE Transactions on Vehicular Technology, 49, 2013–2026. doi:10.1109/25.892603.CrossRefGoogle Scholar
  28. Rescorla, E. (1999). Diffie–Hellman key agreement method. IETF RFC 2631.Google Scholar
  29. Smith, K., Quelhas, P., & Gatica-Perez, D. (2006). Detecting abandoned luggage items in a public space. In: Proceedings of the 9th IEEE International Workshop on Performance Evaluation in Tracking and Surveillance (PETS '06), 75-82.Google Scholar
  30. Snidaro, L., Foresti, G. L., & Piciarelli, C. (2008). Automatic video surveillance of harbour structures. In E. Shahbazian, G. Rogova, & M. J. DeWeert (Eds.), Harbour protection through data fusion technologies (pp. 223–231). Netherlands: Springer.Google Scholar
  31. Stringa, E., & Regazzoni, C. S. (2000). Real-time video-shot detection for scene surveillance applications. IEEE Transactions on Image Processing, 9, 69–79. doi:10.1109/83.817599.CrossRefGoogle Scholar
  32. Tan, T. N., Sullivan, G. D., & Baker, K. D. (1994). Recognizing objects on the ground-plane. Image and Vision Computing, 12, 164–172. doi:10.1016/0262-8856(94)90068-X.CrossRefGoogle Scholar
  33. Tian, Y. L., Lu, M., & Hampapur, A. (2005). Robust and efficient foreground analysis for real-time video surveillance. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 1182-1187.Google Scholar
  34. Tian, Y. L., Brown, L., Hampapur, A., Lu, M., Senior, A., & Shu, C. F. (2008). IBM smart surveillance system (S3): event based video surveillance system with an open and extensible framework. Machine Vision and Applications, 19, 315–327. doi:10.1007/s00138-008-0153-z.CrossRefGoogle Scholar
  35. Venetianer, P. L., Zhang, Z., Yin, W., & Liptop, A. J. (2007). Stationary target detection using the objectvideo surveillance system. In: Proceedings of the IEEE International Conference on Advanced Video and Signal-Based Surveillance, 242-247.Google Scholar
  36. Wang, C. H., Chang, R. I., & Ho, J. M. (2003). An effective communication model for collaborative commerce of web-based surveillance services. In: Proceedings of the IEEE Conference on Electronic Commerce (CEC’03), 40-44.Google Scholar
  37. Wang, C. H., Li, M. W., & Liao, W.J. (2007). A distributed key-changing mechanism for secure voice over IP (VOIP) service. In: Proceedings of the 2007 IEEE International Conference on Multimedia & Expo (ICME 2007), 895-898.Google Scholar
  38. Wang, T. C., Wang, C. H., Chang, R. I., & Ho, J. M. (2008). Ubiquitous video surveillance service with secure forwarding agents. In: IEICE 14th Asia-Pacific Conference on Communications (APCC2008). 14–16.Google Scholar
  39. Zink, M., Schmitt, J., & Steinmetz, R. (2005). Layer-encoded video in scalable adaptive streaming. IEEE Transactions on Multimedia, 7, 75–84. doi:10.1109/TMM.2004.840595.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ray-I Chang
    • 1
  • Te-Chih Wang
    • 1
  • Chia-Hui Wang
    • 2
  • Jen-Chang Liu
    • 3
  • Jan-Ming Ho
    • 4
  1. 1.Department of Engineering Science and Ocean EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of Computer Science and Information EngineeringMing Chuan UniversityTaoyuanTaiwan
  3. 3.Department of Computer Science and Information EngineeringNational Chi Nan UniversityNantouTaiwan
  4. 4.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

Personalised recommendations