A Prototype for Anomaly Detection in Video Surveillance Context

  • F. Persia
  • D. D’Auria
  • G. Sperlí
  • A. Tufano
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 532)


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.


Video surveillance Anomaly detection Activity detection Unexplained activities 


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Petersen, J.K.: Understanding Surveillance Technologies. CRC Press, Boca Raton (2001) zbMATHGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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) CrossRefGoogle Scholar
  7. 7.
    Siebel, N.T., Maybank, S.: The advisor visual surveillance system. In: ECCV 2004 Workshop Applications of Computer Vision (ACV 2004) (2004)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Persia, F., D’Auria, D.: An application for finding expected activities in medial context scientific databases. In: SEBD 2014, pp. 77–88 (2014)Google Scholar
  10. 10.
    D’Auria, D., Persia, F.: Automatic evaluation of medical doctors’ performances while using a cricothyrotomy simulator. In: IRI 2014, pp. 514–519 (2014)Google Scholar
  11. 11.
    D’Auria, D., Persia, F.: Discovering expected activities in medical context scientific databases. In: DATA 2014, pp. 446–453 (2014)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • F. Persia
    • 1
  • D. D’Auria
    • 1
  • G. Sperlí
    • 1
  • A. Tufano
    • 2
  1. 1.Dipartimento di Ingegneria Elettrica e Tecnologie dell’InformazioneUniversity of Naples Federico IINaplesItaly
  2. 2.Universitá Telematica PegasoNaplesItaly

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