Abstract
Security is tedious, complex and tough job in today’s digitized world. An attempt is made to study and propose an intelligent system for surveillance. Surveillance camera systems are used for monitoring and controlling the security. Anomaly detection techniques are proposed for designing the intelligent control system. In the paper challenges in detection and processing of anomaly in surveillance systems are discussed and analyzed. Major components related to an anomaly detection technique of camera control system are proposed in the paper. Surveillance data is generated through camera, and then this data is transmitted over the network to the storage. Processing is to be done on real time basis and if there is any anomaly detected, the system must produce an alert. This paper is an attempt to study soft computing approaches for anomaly detection.
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Zhang, C., Chen, W., Chen, X., Yang, L., Johnstone, J.: A multiple instance learning and relevance feedback framework for retrieving abnormal incidents in surveillance videos. J. Multimedia 5(4), 310–321 (2010)
Ye, Y., Ci, S., Katsaggelos, A.K., Liu, Y., Qian, Y.: Wireless video surveillance: a survey. In: Access, IEEE, vol. 1, pp. 646–660 (2013). doi:10.1109/ACCESS.2013.2282613
Kraiman, J.B., Arouh, S.L., Michael, L.W.: Automated anomaly detection processor. In: Proceedings SPIE 4716, Enabling technology for Simulation Science VI, p. 128, 15 Jul 2002
Saini, D.K.: Security concerns of object oriented software architectures. Int. J. Comput. Appl. 40(11), 41–48 (2012)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Survey 41, 15 (2009)
Wang, Y.-K., Fan, C.-T., Cheng, K.-Y., Deng, P.S.: Real-time camera anomaly detection for real-world video surveillance. In: Proceedings of Machine Learning and Cybernetics (ICMLC) Conference IEEE, vol. 4, pp. 1520–1525, Jul 2011
Li, H., Achim, A., Bull, D.: Unsupervised video anomaly detection using feature clustering. IET Signal Proc. 6, 521–533 (2012)
Maybury, M.: Information fusion and anomaly detection with uncalibrated cameras in video surveillance. In: Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring, vol. 1, pp. 201–216. Wiley-IEEE Press (2011). doi:10.1002/9781118219546.ch13
Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18–32 (2014). doi:10.1109/TPAMI.2013.111
Xiao, T., Zhang, C., Zha, H.: Learning to detect anomalies in surveillance video. Signal Process. Lett. IEEE 22(9), 1477–1481 (2015). doi:10.1109/LSP.2015.2410031
Nguyen, V., Dinh P., Duc-Son, P., Svetha, V.: Bayesian nonparametric approaches to abnormality detection in video surveillance. Ann. Data Sci. 2(1), 21–41 (2015). doi:10.1007/s40745-015-0030-3
Saini, D.K., Saini, H.: Proactive cyber defense and reconfigurable framework for cyber security. Int. Rev. Comput. Softw. (IRCOS) 2(2), 89–98 (2007) (Italy)
Steinwart, I., Hush, D.R., Scovel, H.: A classification framework for anomaly detection. J. Mach. Learn. Res. (2005)
Mazel, J., Casas, P., Fontugne, R., Fukuda, K., Owezarski, P.: Hunting attacks in the dark: clustering and correlation analysis for unsupervised anomaly detection. Int. J. Netw. Manage. 25(5), 283–305 (2015)
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Saini, D.K., Ahir, D., Ganatra, A. (2016). Techniques and Challenges in Building Intelligent Systems: Anomaly Detection in Camera Surveillance. In: Satapathy, S., Das, S. (eds) Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2. Smart Innovation, Systems and Technologies, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-319-30927-9_2
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DOI: https://doi.org/10.1007/978-3-319-30927-9_2
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