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Cooperative Video-Surveillance Framework in Internet of Things (IoT) Domain

  • A. F. Santamaria
  • P. Raimondo
  • N. Palmieri
  • M. Tropea
  • F. De Rango
Chapter
Part of the Internet of Things book series (ITTCC)

Abstract

In this chapter a cooperative heterogeneous system for an enhanced video-surveillance service will be presented. Edge and fog computing architectures make possible the realization of even more complex and distributed services. Moreover, the distribution of sensors and devices gives us the possibility to increase the knowledge of the monitored environments by exploiting Machine to Machine (M2M) communications protocols and their architectures. The rapid growth of IoT increased the number of the smart devices able to acquire, actuate and exchange information in a smart way. In this chapter, the main issues related to the design of an architecture for a smart cooperative video-surveillance system will be presented. The end-system shall exploit edge and fog computing for video-analytics services and communication protocols for cameras data exchange. Finally, all systems together realize a cooperative tracking among cameras that involves detection and tracking techniques to work jointly. At the end a detected anomaly can be followed among cameras generating alerting and notifying messages that will be sent to the designed human interaction system without explicit human interactions in the detection, tracking and system managing processes.

References

  1. 1.
    Z. Shao, J. Cai, Z. Wang, Smart monitoring cameras driven intelligent processing to big surveillance video data. IEEE Trans. Big Data 4(1), 105–116 (2018).  https://doi.org/10.1109/TBDATA.2017.2715815CrossRefGoogle Scholar
  2. 2.
    F. Cicirelli, A. Guerrieri, G. Spezzano, A. Vinci, An edge-based platform for dynamic smart city applications. Future Gener. Comput. Syst. 76, 106–118 (2017)CrossRefGoogle Scholar
  3. 3.
    N.T. Siebel, Design and Implementation of People Tracking Algorithms for Visual Survelliance Applications (University of Reading, 2003)Google Scholar
  4. 4.
    D. LI, Low cost eye tracking for human computer interaction, http://thirtysixthspan.com/openEyes/MS-Dongheng-Li-2006.pdf
  5. 5.
    S. Ali, M. Shah, Floor fields for tracking in high density crowd scenes, in Proceedings of ECCV, 2008Google Scholar
  6. 6.
    C. Liu, J. Yuen, A. Torralba, J. Sivic, W.T. Freeman, Sift flow, in Proceedings of ECCV, 2008Google Scholar
  7. 7.
    J. Yuen, A. Torralba, A data-driven approach for event prediction, in Proceedings of ECCV, 2010Google Scholar
  8. 8.
    A. Molinaro, F. De Rango, S. Marano, M. Tropea, A scalable framework for in IP-oriented terrestrial-GEO satellite networks. IEEE Commun. Mag. 43(4), 130–137 (2005)CrossRefGoogle Scholar
  9. 9.
    F. De Rango, M. Tropea, P. Fazio, S. Marano, Call admission control for aggregate MPEG-2 traffic over multimedia geo-satellite networks. IEEE Trans. Broadcast. 54(3), 612–622 (2008)CrossRefGoogle Scholar
  10. 10.
    F. De Rango, F. Veltri, P. Fazio, S. Marano, Two-level trajectory-based routing protocol for vehicular ad hoc networks in freeway and Manhattan environments. J. Netw. 4(9), 866–880 (2009)Google Scholar
  11. 11.
    F. De Rango, M. Gerla, S. Marano, A scalable routing scheme with group motion support in large and dense wireless ad hoc networks. Comput. Electr. Eng. 32(1–3), 224–240 (2006)CrossRefGoogle Scholar
  12. 12.
    F. De Rango, M. Tropea, A.F. Santamaria, S. Marano, Multicast QoS core-based tree routing protocol and genetic algorithm over an HAP-satellite architecture. IEEE Trans. Veh. Technol. 58(8), 4447–4461 (2009)CrossRefGoogle Scholar
  13. 13.
    B. Zhou, Y.Z. Lee, M. Gerla, F. De Rango, GeoLANMAR: a scalable routing protocol for ad hoc networks with group motion. Wirel. Commun. Mob. Comput. 6(7), 989–1002 (2006)CrossRefGoogle Scholar
  14. 14.
    M. Satyanarayanan et al., The case for VM-Based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14–23 (2009)CrossRefGoogle Scholar
  15. 15.
    F. Bonomi et al., Fog computing and its role in the internet of things, in Proceedings of 1st edn. MCC Workshop Mobile Cloud Computing (MCC 12), 2012, pp. 13–15Google Scholar
  16. 16.
    J. Gettys, K. Nichols, Bufferbloat: Dark Buffers in the Internet, vol. 9, no. 11 (ACM Queue, 2011)Google Scholar
  17. 17.
    M. Satyanarayanan, The emergence of edge computing. Computer 50(1), 30–39 (2017).  https://doi.org/10.1109/MC.2017.9CrossRefGoogle Scholar
  18. 18.
    A.H.M. Amin, N.M. Ahmad, A.M.M. Ali, Decentralized face recognition scheme for distributed video surveillance in IoT-cloud infrastructure, in IEEE Region 10 Symposium (TENSYMP), Bali, vol. 2016, 2016, pp. 119–124.  https://doi.org/10.1109/TENCONSpring.2016.7519389
  19. 19.
    H.S. Parekh, D.G. Thakore, U.K. Jaliya, A survey on object detection and tracking methods. Int. J. Innov. Res. Comput. Commun. Eng. 2(2) (2014)Google Scholar
  20. 20.
    N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, vol. 1, 2005, pp. 886–893.  https://doi.org/10.1109/CVPR.2005.177
  21. 21.
    S. Miguet A. Ilyas, M. Scuturici, Inter-camera color calibration for object re-identification and tracking, in 2010 International Conference of Soft Computing and Pattern Recognition, 2010Google Scholar
  22. 22.
  23. 23.
    K. Balani, S. Deshpande, R. Nair, V. Rane, Human detection for autonomous vehicles, in IEEE International Transportation Electrification Conference (ITEC), Chennai, vol. 2015, 2015, pp. 1–5.  https://doi.org/10.1109/ITEC-India.2015.7386891
  24. 24.
    C.G. Saneem Ahmed, S. Saravanakumar, A. Vadivel, Multiple human object tracking using background subtraction and shadow removal techniques, in Signal and Image Processing, 2010Google Scholar
  25. 25.
    M.A. AlGhamdi, M.A. Khan, S.H. AlMotiri, Automatic motion tracking of a human in a surveillance video, in IEEE First International Smart Cities Conference (ISC2), Guadalajara, vol. 2015, 2015, pp. 1–4.  https://doi.org/10.1109/ISC2.2015.7366165
  26. 26.
    G. Sindhuja, S. Devi, M. Renuka, Comparative analysis of mean shift in object tracking, in Power Control Communication and Computational Technologies for Sustainable Growth, 2016, pp. 283–287Google Scholar
  27. 27.
    H. Ji, Real time robust 11 tracker using accelerated proximal gradient approach, in IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1830–1837Google Scholar
  28. 28.
    J.F. Henriques, C. Rui, P. Martins, J. Batista, High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)CrossRefGoogle Scholar
  29. 29.
    S. Hare, S. Golodetz, A. Saffari, V. Vineet, M.M. Cheng, S.L. Hicks, P.H.S. Torr, Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096 (2016)Google Scholar
  30. 30.
    F. Feng, X.J. Wu, T. Xu, Object tracking with kernel correlation filters based on mean shift, in International Smart Cities Conference (ISC2), Wuxi, vol. 2017, 2017, pp. 1–7.  https://doi.org/10.1109/ISC2.2017.8090863
  31. 31.
    R.T. Collins, A.J. Lipton, H. Fujiyoshi, T. Kanade, Algorithms for cooperative multisensory surveillance, in Proceedings of IEEE, Oct 2001Google Scholar
  32. 32.
    J. Mallett, V.M. Bove, Eye society, in Proceedings of IEEE ICME, 2003Google Scholar
  33. 33.
    C. Hong Lin, T. LV, W. Wolf, B. Ozer, A peer to peer architecture for distributed real time gesture recognition, in Proceedings of International Conference on Multimedia and Exhibition (IEEE 2004)Google Scholar
  34. 34.
    M. Bramberger, M. Quartisch, T. Winkler, B. Rinner, H. Scwabach, Integrating multicamera tracking into a dynamic task allocation system for smart cameras, in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2005Google Scholar
  35. 35.
    L. McMillan, G. Bishop, Plenoptic modeling: and image-based rendering system, in Proceedings of ACM SIGGRAPHGoogle Scholar
  36. 36.
    B. Leibe, E. Koller-Meier, L. Van Gool, M. Breitenstein, F. Reichlin, Online Multi-Person Tracking-by-Detection from a Single, Uncalibrated Camera. Pattern Anal. Mach. Intell. (2010)Google Scholar
  37. 37.
    B. Schiele M. Andriluka, S. Roth, People-tracking-by-detection and peopledetection-by-tracking. Comput. Vis. Pattern Recognit. (2008)Google Scholar
  38. 38.
    E. Cassano, F. Florio, F. De Rango, S. Marano, A performance comparison between ROC-RSSI and trilateration localization techniques for WPAN sensor networks in a real outdoor testbed, in Wireless Telecommunications Symposium, WTS 2009, Prague, Czech Republic, 22–24 Apr 2009, pp. 1–8Google Scholar
  39. 39.
    P. Simoens et al., Scalable crowd-sourcing of video from mobile devices, in Proceedings of 11th International Conference Mobile Systems Applications and Services (MobiSys 13), 2013, pp. 139-152Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • A. F. Santamaria
    • 1
  • P. Raimondo
    • 1
  • N. Palmieri
    • 1
  • M. Tropea
    • 1
  • F. De Rango
    • 1
  1. 1.DIMES - University of CalabriaRendeItaly

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