Abstract
Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.
Similar content being viewed by others
References
Why Hypertable? | Hypertable-Big Data. Big Performance. URL http://hypertable.com/why_hypertable/
Ahmed, T., Pathan, A.S., Ahmed, S.: Adaptive algorithms for automated intruder detection in surveillance networks. In: 2014 International Conference on Advances in Computing, Communications and Informatics ICACCI, pp. 2775–2780 (2014). doi:10.1109/ICACCI.2014.6968617
Alamri, A., Hossain, M.S., Almogren, A., Hassan, M.M., Alnafjan, K., Zakariah, M., Seyam, L., Alghamdi, A.: QoS-adaptive service configuration framework for cloud-assisted video surveillance systems. Multimedia Tools and Applications pp. 1–16 (2015). doi:10.1007/s11042-015-3074-7. http://0-link.springer.com.fama.us.es/article/10.1007/s11042-015-3074-7
Angin, P., Bhargava, B., Helal, S.: A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation. In: 2010 Eleventh International Conference on Mobile Data Management (MDM), pp. 396–401 (2010). doi:10.1109/MDM.2010.71
Appeldoom, R.: A volumetric object-oriented world model applied in robot navigation. Master Thesis, Eindhoven University of Technology, Eindhoven (2014)
Kim, B., Bhaskar, K.P.: Special section on emerging multimedia technology for smart surveillance system with iot environment. J. Supercomput. 73(3), 923–925 (2017). doi:10.1007/s11227-016-1939-9
Ben Hamida, A., Koubaa, M., Ben Amar, C., Nicolas, H.: Toward scalable application-oriented video surveillance systems. Sci. Inf. Conf. (SAI) 2014, 384–388 (2014). doi:10.1109/SAI.2014.6918215
Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: A Distributed Storage System for Structured Data. ACM Trans. Comput. Syst. 26(2), 4:1–4:26 (2008). doi:10.1145/1365815.1365816
Charfi, E., Chaari, L., Kamoun, L.: PHY/MAC enhancements and QoS mechanisms for very high throughput WLANs: a survey. IEEE Commun. Surveys Tutor. 15(4), 1714–1735 (2013). doi:10.1109/SURV.2013.013013.00084
Dogmus, Z., Papantoniou, A., Kilinc, M., Yildirim, S., Erdem, E., Patoglu, V.: Rehabilitation robotics ontology on the cloud. In: 2013 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–6 (2013). doi:10.1109/ICORR.2013.6650415
Elfring, J., van den Dries, S., van de Molengraft, M.J.G., Steinbuch, M.: Semantic world modeling using probabilistic multiple hypothesis anchoring. Robotics and Autonomous Systems 61(2), 95–105 (2013). doi:10.1016/j.robot.2012.11.005, http://www.sciencedirect.com/science/article/pii/S0921889012002163
Ghose, A., Chakravarty, K., Agrawal, A.K., Ahmed, N.: Unobtrusive Indoor Surveillance of Patients at Home Using Multiple Kinect Sensors. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, SenSys ’13, pp. 40:1–40:2. ACM, New York, NY, USA (2013). doi:10.1145/2517351.2517412
Guizzo, E.: Robots with their heads in the clouds. IEEE Spectrum 48(3), 16–18 (2011). doi:10.1109/MSPEC.2011.5719709
Hamida, A.B., Koubaa, M., Nicolas, H., Amar, C.B.: Video surveillance system based on a scalable application-oriented architecture. Multimedia Tools and Applications pp. 1–27 (2015). doi:10.1007/s11042-015-2987-5, http://0-link.springer.com.fama.us.es/article/10.1007/s11042-015-2987-5
Hassan, M., Hossain, M., Al-Qurishi, M.: Cloud-based mobile IPTV terminal for video surveillance. In: 2014 16th International Conference on Advanced Communication Technology (ICACT), pp. 876–880 (2014). doi:10.1109/ICACT.2014.6779086
Park, H.D.: Scalable architecture for an automated surveillance system using edge computing. J. Supercomput. 73(3), 926 (2017). doi:10.1007/s11227-016-1750-7
Hossain, M.A.: Framework for a cloud-based multimedia surveillance system. International Journal of Distributed Sensor Networks 10(5), 135,257 (2014). doi:10.1155/2014/135257
Iigo-Blasco, P., Diaz-del Rio, F., Romero-Ternero, M.C., Cagigas-Muiz, D., Vicente-Diaz, S.: Robotics software frameworks for multi-agent robotic systems development. Robot. Auton. Syst. 60(6), 803–821 (2012). doi:10.1016/j.robot.2012.02.004
Khetrapal, A., Ganesh, V.: Hbase and hypertable for large scale distributed storage systems. Dept. of Computer Science, Purdue University (2006). Available at: urlhttp://cloud.pubs.dbs.uni-leipzig.de/node/46
Limna, T., Tandayya, P.: A flexible and scalable component-based system architecture for video surveillance as a service, running on infrastructure as a service. Multimedia Tools and Applications pp. 1–27 (2014). doi:10.1007/s11042-014-2373-8, http://0-link.springer.com.fama.us.es/article/10.1007/s11042-014-2373-8
Liu, J., Nishimura, S., Araki, T.: Wally: A Scalable Distributed Automated Video Surveillance System with Rich Search Functionalities. In: Proceedings of the 22Nd ACM International Conference on Multimedia, MM ’14, pp. 729–730. ACM, New York, NY, USA (2014). doi:10.1145/2647868.2654872
Lunenburg, J., van den Dries, S., Bento Ferreira, L., van de Molengraft, M.J.G.: Tech United Eindhoven @Home 2015 Team Description Paper. Eindhoven University of Technology, Eindhoven, Tech. rep. (2015)
Alsmirat, M.A., Jararweh, Y.: Internet of surveillance: a cloud supported large-scale wireless surveillance system. J. Supercomput. 73(3), 973 (2017). doi:10.1007/s11227-016-1857-x
Martins, G.: Reducing Communication Delay Variability for a Group of Robots. Ph.D. thesis, University of Denver, Denver, CO, USA (2013)
Meinel, L., Findeisen, M., Hes, M., Apitzsch, A., Hirtz, G.: Automated real-time surveillance for ambient assisted living using an omnidirectional camera. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 396–399 (2014). doi:10.1109/ICCE.2014.6776056
Neal, D., Rahman, S.M.: Video surveillance in the cloud-computing? In: 2012 7th International Conference on Electrical and Computer Engineering, pp. 58–61 (2012). doi:10.1109/ICECE.2012.6471484
Oh, J.M., Moon, N., Hong, S.: Trajectory based database management for intelligent surveillance system with heterogeneous sensors. Multimedia Tools and Applications pp. 1–16 (2015). DOI 10.1007/s11042-015-2725-z. http://link.springer.com/article/10.1007/s11042-015-2725-z
Ozalp Babaoglu, Moreno Marzolla: Escape From the Data Center: The Promise of Peer-to-Peer Cloud Computing. IEEE Spectrum Magazine (2014)
Prati, A., Vezzani, R., Fornaciari, M., Cucchiara, R.: Intelligent video surveillance as a service. In: Atrey, P.K., Kankanhalli, M.S., Cavallaro A. (eds.) Intelligent multimedia surveillance, pp. 1–16. Springer Berlin Heidelberg (2013). doi:10.1007/978-3-642-41512-8_1
Riazuelo, L., Civera, J., Montiel, J.M.M.: C2tam: a cloud framework for cooperative tracking and mapping. Robot. Auton. Syst. 62(4), 401–413 (2014). doi:10.1016/j.robot.2013.11.007
del Rio, F.D., Salmeron-Garcia, J., Sevillano, J.L.: Extending amdahl’s law for the cloud computing era. Computer 49(2), 14–22 (2016). doi:10.1109/MC.2016.49
RTC Group: Cloud Based Surveillance System (2015). URL https://www.youtube.com/playlist?list=PLgUj9dv84AxAVFttquWg1VPaza5no5b2K
Seo, K.T., Hwang, H.S., Moon, I.Y., Kwon, O.Y., Kim, B.J.: Performance comparison analysis of linux container and virtual machine for building cloud. Adv. Sci. Technol. Lett. 66(105–111), 2 (2014)
Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: Vision and challenges. IEEE Internet of Things Journal pp. 637–646 (2016). doi:10.1109/JIOT.2016.2579198. http://ieeexplore.ieee.org/document/7488250/
Shim, J., Lim, Y., Park, J.: Architectural Design of Cloud Gateway in Smart Surveillance System. In: Proceedings of the 2013 Research in Adaptive and Convergent Systems, RACS ’13, pp. 261–266. ACM, New York, NY, USA (2013). doi:10.1145/2513228.2513320
Song, B., Tian, Y., Zhou, B.: Design and Evaluation of Remote Video Surveillance System on Private Cloud. In: 2014 International Symposium on Biometrics and Security Technologies (ISBAST), pp. 256–262 (2014). doi:10.1109/ISBAST.2014.7013131
Waibel, M., Beetz, M., Civera, J., D’Andrea, R., Elfring, J., Galvez-Lopez, D., Haussermann, K., Janssen, R., Montiel, J.M.M., Perzylo, A., Schiessle, B., Tenorth, M., Zweigle, O., van de Molengraft, R.: RoboEarth. IEEE Robot. Autom. Mag. 18(2), 69–82 (2011). doi:10.1109/MRA.2011.941632
Zhang, T., Chowdhery, A., Bahl, P.V., Jamieson, K., Banerjee, S.: The Design and Implementation of a Wireless Video Surveillance System. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, MobiCom ’15, pp. 426–438. ACM, New York, NY, USA (2015). doi:10.1145/2789168.2790123
Acknowledgements
The work shown in this paper has been supported by the Spanish grant (supported by the Ministerio de Economía y Competitividad and the European Regional Development Fund) COFNET (Event-based Cognitive Visual and Auditory Sensory Fusion, TEC2016-77785-P) and by Andalusian Regional Excellence Research Project grant (with support from the European Regional Development Fund) MINERVA (P12-TIC-1300).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Salmerón-García, J.J., van den Dries, S., Díaz-del-Río, F. et al. Towards a cloud-based automated surveillance system using wireless technologies. Multimedia Systems 25, 535–549 (2019). https://doi.org/10.1007/s00530-017-0558-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00530-017-0558-5