Abstract
Nowadays, new generation of video surveillance systems integrates lots of heterogeneous cameras to collect, process, and analyze video for detecting the objects of potential security threats. The existing systems tend to reach the limit in terms of scalability, data access anywhere, video processing overhead, and massive storage requirements. A novel cloud computing can provide scalable and powerful techniques for large-scale storage, processing, and dissemination of video data. Furthermore, the integration of cloud computing and video processing technology offers more possibilities for efficient deployment of surveillance systems. This paper deploys the framework of a cloud-based video surveillance system and proposes an EFD-GMM approach for object detection in the overhead video processing. A prototype surveillance system is also designed to validate the proposed approach. It finally shows that the proposed approach is more efficient than GMM in video processing of cloud-based system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raty, T.D.: Survey on contemporary remote surveillance systems for public safety. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 40(5), 493–515 (2010)
Ren, Y.J., Shen, J., Wang, J., Han, J., Lee, S.: Mutual verifiable provable data auditing in public cloud storage. J. Internet Technol. 16(2), 317–323 (2015)
Karimaa, A.: Video surveillance in the cloud: dependability analysis. In: Proceedings of 4th International Conference on Dependability, pp. 92–95 (2011)
Hossain, M.S., Hassan, M.M., Qurishi, M.A., Alghamdi, A..: Resource allocation for service composition in cloud-based video surveillance platform. In: Proceedings of IEEE International Conference on Multimedia and Expo Workshops, pp. 408–412 (2012)
Lin, C.F., Yuan, S.M., Leu, M.C., Tsai, C.T.: A framework for scalable cloud video recorder system in surveillance environment. In: Proceedings of 9th International Conference on Ubiquitous Intelligence & Computing and Autonomic & Trusted Computing, pp. 655–660 (2012)
Hossain, M.A.: Analyzing the suitability of cloud based multimedia surveillance systems. In: Proceedings of 15th IEEE International Conference on High Performance Computing and Communications (2013)
Neal, D., Rahman, S.: Video surveillance in the cloud. J. Crypt. Inf. Secur. 2(3) (2015)
Sabahi, F.: Cloud computing security threats and responses. In: Proceedings of 3rd International Conference on Communication Software and Networks, pp. 245–249 (2011)
Venters, W., Whitley, E.A.: A critical review of cloud computing: researching desires and realities. J. Inf. Technol. 27(3), 179–197 (2012)
Hossain, M.A.: Framework for a Cloud-based Multimedia Surveillance System. J. Distrib. Sens. Netw. (2014)
Cucchiara, R., Prati, A., Vezzani, R.: Designing video surveillance systems as services. In: Proceedings of 2nd Workshop on Video Surveillance Projects, Italy (2011)
Li, J., Li, X.L., Yang, B., Sun, X.M.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. PAMI 22(8), 888–905 (2000)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Heidelberg (2009)
Zhang, B.C., Perina, A., Li, Z.G., Murino, V., Liu, J.Z., Ji, R.R.: Bounding multiple gaussians uncertainty with application to object tracking. IJCV 118, 364–379 (2016)
Zhang, B.C., Li, Z.G., Perina, A., Bue, A.D., Murino V.: Adaptive local movement modelling (ALMM) for object tracking. In: IEEE Transactions on CSVT (2016)
Zhang, B.C., Perina, A., Murino, V., Bue, A.D.: Sparse representation classification with manifold constraints transfer. In: Proceedings of CVPR, pp. 4557–4565 (2015)
Mahmood, A.M., Maras, H.H., Elbasi, E.: Measurement of edge detection algorithms in clean and noisy environment. In: Proceedings of 8th International Conference on Application of Information and Communication Technologies, pp. 1–6 (2014)
Shen, R.: Building a cloud-enabled file storage infrastructure. Tech Republic White Paper, F5 Network (2013)
Sotomayor, B., Montero, R.S., Lorente, I.M., Foster, I.: Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput. 13(5), 14–22 (2009)
Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: Proceedings of 10th IEEE ACM International Symposium on Cluster, Cloud, and Grid Computing, pp. 826–831 (2010)
Liu, X.D., Yu, Y., Liu, B., Li, Z.: Bowstring-based dual-threshold computation method for adaptive canny edge detector. In: Proceedings of International Conference of Image and Vision Computing, New Zealand, pp. 13–18 (2013)
Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S.: Incremental support vector learning for ordinal regression. Trans. Neural Netw. Learn. Syst. 26(7), 1403–1416 (2015)
Pan, Z.Q., Kwong, S., Sun, M.T., Lei, J.J.: Early MERGE mode decision based on motion estimation and hierarchical depth correlation for HEVC. IEEE Trans. Broadcast. 60(2), 405–412 (2014)
Acknowledgement
The work was supported in part by the Natural Science Foundation of China under Contract 61272052, 61473086, 61672079, 61601466, in part by PAPD, in part by CICAEET, and in part by the National Basic Research Program of China under Grant 2015CB352501. The work of B. Zhang was supported by the Program for New Century Excellent Talents University within the Ministry of Education, China, and Beijing Municipal Science & Technology Commission Z161100001616005. Ba-chang Zhang is the corresponding author.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Li, C., Su, J., Zhang, B. (2016). Cloud-Based Video Surveillance System Using EFD-GMM for Object Detection. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10039. Springer, Cham. https://doi.org/10.1007/978-3-319-48671-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-48671-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48670-3
Online ISBN: 978-3-319-48671-0
eBook Packages: Computer ScienceComputer Science (R0)