Abstract
Security has been raised at major public buildings in the most famous and crowded cities all over the world following the terrorist attacks of the last years, the latest one at the Bardo museum in the centre of Tunis. For that reason, video surveillance systems have become more and more essential for detecting and hopefully even prevent dangerous events in public areas. In this paper, we present a prototype for anomaly detection in video surveillance context. The whole process is described, starting from the video frames captured by sensors/cameras till at the end some well-known reasoning algorithms for finding potentially dangerous activities are applied. The conducted experiments confirm the efficiency and the effectiveness achieved by our prototype.
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References
Albanese, M., Molinaro, C., Persia, F., Picariello, A., Subrahmanian, V.S.: Discovering the Top-k unexplained sequences in time-stamped observation data. IEEE Trans. Knowl. Data Eng. (TKDE) 26(3), 577–594 (2014)
Albanese, M., Molinaro, C., Persia, F., Picariello, A., Subrahmanian, V.S.: Finding unexplained activities in video. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 1628–1634 (2011)
Petersen, J.K.: Understanding Surveillance Technologies. CRC Press, Boca Raton (2001)
Collins, R., Lipton, A., Kanade, T.K.: Introduction to the special section on video surveillance. IEEE Trans. Patt. Anal. Mach. Intell. 22(8), 745–746 (2000)
Regazzoni, C., Ramesh, V.: Scanning the Issue/Technology Special Issue on Video Communications, Processing, and Understanding for Third Generation Surveillance Systems, University of Genoa, Siemens Corporate Research Inc., University of Udine, IEEE (2001)
Siebel, N.T., Maybank, S.J.: Fusion of multiple tracking algorithms for robust people tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 373–387. Springer, Heidelberg (2002)
Siebel, N.T., Maybank, S.: The advisor visual surveillance system. In: ECCV 2004 Workshop Applications of Computer Vision (ACV 2004) (2004)
Albanese, M., Pugliese, A., Subrahmanian, V.S.: Fast activity detection: indexing for temporal stochastic automaton based activity models. IEEE Trans. Knowl. Data Eng. (TKDE) 25, 360–373 (2013)
Persia, F., D’Auria, D.: An application for finding expected activities in medial context scientific databases. In: SEBD 2014, pp. 77–88 (2014)
D’Auria, D., Persia, F.: Automatic evaluation of medical doctors’ performances while using a cricothyrotomy simulator. In: IRI 2014, pp. 514–519 (2014)
D’Auria, D., Persia, F.: Discovering expected activities in medical context scientific databases. In: DATA 2014, pp. 446–453 (2014)
Dung, P., Chi-Min, O., Soo-Hyung, K., In-Seop, N., Chil-Woo, L.: Object recognition by combining binary local invariant features and color histogram. In: 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 466–470 (2013)
Chaudhary, K., Mae, Y., Kojima, M., Arai, T.: Autonomous acquisition of generic handheld objects in unstructured environments via sequential back-tracking for object recognition. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4953–4958 (2014)
Ubukata, T., Shibata, M., Terabayashi, K., Mora, A., Kawashita, T., Masuyama, G., Umeda, K.: Fast human detection combining range image segmentation and local feature based detection. In: 22nd International Conference on Pattern Recognition (ICPR), pp. 4281–4286 (2014)
Onal, I., Kardas, K., Rezaeitabar, Y., Bayram, U., Bal, M., Ulusoy, I., Cicekli, N.K.: A framework for detecting complex events in surveillance videos. In: 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–6 (2013)
Zin, T.T., Tin, P., Hama, H., Toriu, T.: An integrated framework for detecting suspicious behaviors in video surveillance. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series (2014)
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Persia, F., D’Auria, D., Sperlí, G., Tufano, A. (2015). A Prototype for Anomaly Detection in Video Surveillance Context. In: Fujita, H., Guizzi, G. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2015. Communications in Computer and Information Science, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-319-22689-7_40
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DOI: https://doi.org/10.1007/978-3-319-22689-7_40
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