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Scalable architecture for an automated surveillance system using edge computing

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Abstract

Over the past few years, the Internet of Things has gone from theoretical concept to our everyday living experience. The explosive growth of sensor streams also leads to a new paradigm of edge computing. In the surveillance system, edge-based automation is crucial to get fast response for fast data analytics among connected devices. In this paper, we propose an automated surveillance system to improve robustness and intelligence. Our scalable architecture is an alternative way of reducing the server resource and wireless network limitation.

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References

  1. Castanedo F, Patricio MA, Garcia J, Molina JM (2006) Extending surveillance systems capabilities using BDI cooperative sensor agents. In: Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, ACM, pp 131–138

  2. Valera M, Velastin SA (2005) Intelligent distributed surveillance systems: a review. In: Vision, image and signal processing, IEEE proceedings, IET, vol 152, no 2, pp 192–204

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

    Article  Google Scholar 

  4. Oberti F, Ferrari G, Regazzoni CS (2001) Recognition driven burst transmissions in distributed third generation surveillance systems. In: International conference on image analysis and processing, pp 490–494

  5. Kolarow A, Schenk K, Eisenbach M, Dose M, Brauckmann M, Debes K, Gross HM (2013) Apfel: the intelligent video analysis and surveillance system for assisting human operators. In: 10th IEEE international conference on, advanced video and signal based surveillance (AVSS), pp 195–201

  6. Choi J, Moon D, Yoo J (2015) Robust multi-person tracking for real-time intelligent video surveillance. ETRI J 37(3):551–561

    Article  Google Scholar 

  7. Behera RK, Kharade P, Yerva S, Dhane P, Jain A, Kutty K (2012) Multi-camera based surveillance system. In: IEEE, information and communication technologies (WICT), 2012 World Congress on, pp 102–108

  8. Chen WT, Chen PY, Lee WS, Huang CF (2008) Design and implementation of a real time video surveillance system with wireless sensor networks. In: IEEE, vehicular technology conference, VTC Spring 2008, pp 218–222

  9. Schierl T, Hannuksela MM, Wang YK, Wenger S (2012) System layer integration of high efficiency video coding (HEVC). IEEE Trans Circuit Syst Video Technol 22(12):1871–1884

    Article  Google Scholar 

  10. Psannis KE, Hadjinicolaou M, Krikelis A (2006) MPEG2 streaming of full interactive content. IEEE Trans Circuit Syst Video Technol 16(2):280285

    Article  Google Scholar 

  11. Wenger S (2003) H.264/AVC over IP. IEEE Trans Circuits Syst 13(7):645–656

    Google Scholar 

  12. Stockhamme T, Hannuksela MM, Wiegand T (2003) H.264/AVC in wireless environments. IEEE Trans Circuits Syst Video Technol 13(7):657–673

    Article  Google Scholar 

  13. Psannis K, Ishibashi Y (2008) Efficient flexible macroblock ordering technique. IEICE Trans Commun E91B(08):2692–2701

    Article  Google Scholar 

  14. Psannis KE (2015) HEVC in wireless environments. J RealTime Image Process

  15. Psannis K, Ishibashi Y (2006) Impact of video coding on delay and jitter in 3G wireless video multicast services. EURASIP J Wirel Commun Netw 2006:17, Article ID 24616

  16. Nieminen M, Raty T, Lindholm M (2009) Multi-sensor logical decision making in the single location surveillance point system. In: 4th international conference on IEEE, systems, 2009, ICONS’09, pp 86–90

  17. Monari E, Voth S, Kroschel K (2008) An object-and task-oriented architecture for automated video surveillance in distributed sensor networks. In: IEEE 5th international conference on IEEE, Advanced video and signal based surveillance, AVSS’08, pp 339–346

  18. Zhang T, Chowdhery A, Bahl PV, Jamieson K, Banerjee S (2015) The design and implementation of a wireless video surveillance system. In: Proceedings of the 21st annual international conference on mobile computing and networking, ACM, pp 426–438

  19. LaMothe R (2013) Edge computing. Washington

  20. Severance C (2013) Eben upton: Raspberry Pi. IEEE Society

  21. InfluxDB–an open-source, distributed, time-series database with no external dependencies. https://influxdata.com/. Accessed 02 May 2016

  22. Grafana. http://grafana.org/. Accessed 02 May 2016

  23. Wilson PI, Fernandez J (2006) Facial feature detection using Haar classifiers. J Comput Sci Coll 21(4):127–133

    Google Scholar 

  24. Menezes P, Barreto JC, Dias J (2004) Face tracking based on Haar-like features and eigenfaces. In: 5th IFAC symposium on intelligent autonomous vehicles, Lisbon, Portugal, 5–7 July 2004

  25. Nethogs https://raboof.github.io/nethogs/. Accessed 02 May 2016

  26. The Apache Software Foundation. The Apache HTTP server. http://www.apache.org/. Accessed 02 May 2016

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Acknowledgments

This work was supported by the ICT R&D program of MSIP/IITP [R0126-15-1067, Development of Hierarchical Data Stream Analysis SW Technology for Improving the Realtime Reaction on a CoT (Cloud of Things) Environment].

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Authors and Affiliations

  1. Big Data Intelligence Research Department, Software and Contents Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon, Korea

    Hwin Dol Park, Ok-Gee Min & Yong-Ju Lee

  2. Department of Computer Software, University of Science and Technology, Daejeon, Korea

    Hwin Dol Park & Yong-Ju Lee

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  1. Hwin Dol Park
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  2. Ok-Gee Min
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  3. Yong-Ju Lee
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Correspondence to Yong-Ju Lee.

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Park, H.D., Min, OG. & Lee, YJ. Scalable architecture for an automated surveillance system using edge computing. J Supercomput 73, 926–939 (2017). https://doi.org/10.1007/s11227-016-1750-7

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  • DOI: https://doi.org/10.1007/s11227-016-1750-7

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