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Performance Evaluation of Video Analytics for Surveillance On-Board Trains

  • Valentina Casola
  • Mariana Esposito
  • Francesco Flammini
  • Nicola Mazzocca
  • Concetta Pragliola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)

Abstract

Real-time video-surveillance systems are nowadays widespread in several applications, including public transportation. In those applications, the use of automatic video content analytics (VCA) is being increasingly adopted to support human operators in control rooms. However, VCA is only effective when its performances are such to reduce the number of false positive alarms below acceptability thresholds while still detecting events of interest. In this paper, we report the results of the evaluation of a VCA system installed on a rail transit vehicle. With respect to fixed installations, on-board ones feature specific constraints on camera installation, obstacles, environment, etc. Several VCA performance evaluation metrics have been considered, both frame-based and object-based, computed by a tool developed in Matlab. We compared the results obtained using a commercial VCA system with the ones produced by an open-source one, showing the higher performance of the former in all test conditions.

Keywords

Rail security Video Content Analysis Intelligent video surveillance 

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References

  1. 1.
    Cozzolino, A., Flammini, F., Galli, V., Lamberti, M., Poggi, G., Pragliola, C.: Evaluating the effects of MJPEG compression on motion tracking in metro railway surveillance. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds.) ACIVS 2012. LNCS, vol. 7517, pp. 142–154. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Piero, J.C.: Intelligent Video Results of testing 4 technologies on Madrid Metro. In: Procs. Joint UITP-CUTA International Security Conference, Montreal, Canada, November 11-12 (2009)Google Scholar
  3. 3.
    Lookingbill, A., Antunez, E.R., Erol, B., Hull, J.J., Qifa, K., Moraleda, J.: Ground-Truthed Video Generation from Symbolic Information. In: Multimedia and Expo IEEE International Conference (2007)Google Scholar
  4. 4.
    Yin, F., Makris, D., Velastin, S.A., Orwell, J.: Quantitative evaluation of different aspects of motion trackers under various challenges. In: Quantitative Evaluation of Trackers, Annual of the BMVA (2010)Google Scholar
  5. 5.
  6. 6.
    Thornton, J., Baran-Gale, J., Yahr, A.: An assessment of the video analytics technology gap for transportation facilities. In: IEEE Conference on Technologies for Homeland Security, vol. 135(142), pp. 11–12 (2009)Google Scholar
  7. 7.
    Sacchi, C., Regazzoni, C.S.: A distributed surveillance system for detection of abandoned objects in unmanned railway environments. IEEE Transactions on Vehicular Technology 49(5), 2013–2026 (2000)CrossRefGoogle Scholar
  8. 8.
    Faisal, B., Porikli, F.: Performance evaluation of object detection and tracking systems. In: PETS, vol. 6 (2006)Google Scholar
  9. 9.
    Baumann, A., Boltz, M., Ebling, J., Koeing, M., Loors, H.S., Merkel, M., Niem, W., Warzelhan, J.K., Yu, J.: A Review and Comparison of Measures for Automatic Video Surveillance Systems. EURASIP Journal on Image Video Processing (2008)Google Scholar
  10. 10.
    de Titta, S., Gera, G., Marcenaro, L.: VTrack: Video analytics for automatic video-surveillance. In: 2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 536–538 (2011)Google Scholar
  11. 11.
    Kamijo, S., Takahashi, T., Naito, T., Yoshimitsu, Y.: Framework Study on Behavior Understandings Based on Posture and Location State Transition for Railway Station Security. International Journal of Intelligent Transportation Systems Research, 1–8 (2012)Google Scholar
  12. 12.
    Manohar, V., Soundararajan, P., Raju, H., Goldgof, D., Kasturi, R., Garofolo, J.: Performance evaluation of object detection and tracking in video. In: Proceedings of the 7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16 (2006)Google Scholar
  13. 13.
    Monitzer, A.: Using video surveillance to detect dangerous situations in underground stations by computer vision. In: Proceedings of Scientific Presentation and Communication (2006)Google Scholar
  14. 14.
    Casola, V., Esposito, M., Mazzocca, N., Flammini, F.: Freight train monitoring: A case-study for the pSHIELD project. In: Proceedings of 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS (2012)Google Scholar
  15. 15.
    Casola, V., Gaglione, A., Mazzeo, A.: A reference architecture for sensor networks integration and management. In: Trigoni, N., Markham, A., Nawaz, S. (eds.) GSN 2009. LNCS, vol. 5659, pp. 158–168. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Amato, F., Casola, V., Gaglione, A., Mazzeo, A.: A semantic enriched data model for sensor network interoperability. Journal of Simulation Modelling Practice and Theory 19(8), 1745–1757 (2011)CrossRefGoogle Scholar
  17. 17.
    Bocchetti, G., Flammini, F., Pragliola, C., Pappalardo, A.: Dependable integrated surveillance systems for the physical security of metro railways. In: Third ACM/IEEE International Conference on Distributed Smart Cameras, pp. 1–7. IEEE (2009)Google Scholar
  18. 18.
    Flammini, F., Mazzocca, N., Pappalardo, A., Pragliola, C., Vittorini, V.: Augmenting surveillance system capabilities by exploiting event correlation and distributed attack detection. In: Tjoa, A.M., Quirchmayr, G., You, I., Xu, L. (eds.) ARES 2011. LNCS, vol. 6908, pp. 191–204. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Flammini, F., Gaglione, A., Ottello, F., Pappalardo, A., Pragliola, C., Tedesco, A.: Towards wireless sensor networks for railway infrastructure monitoring. In: Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS), pp. 1–6. IEEE (2010)Google Scholar
  20. 20.
    Buemi, F., Esposito, M., Flammini, F., Mazzocca, N., Pragliola, C., Spirito, M.: Empty Vehicle Detection with Video Analytics. In: Petrosino, A. (ed.) ICIAP 2013, Part II. LNCS, vol. 8157, pp. 731–739. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Garibotto, G., Murrieri, P., Capra, A., De Muro, S., Petillo, U., Flammini, F., Esposito, M., Pragliola, C., Di Leo, G., Lengu, R., Mazzino, N., Paolillo, A., Durso, M., Vertucci, R., Narducci, F., Ricciardi, S., Savastano, M.: White-paper: Industrial Applications of Computer Vision and Pattern Recognition CVPR. To appear in Proceedings of 17th International Conference on Image Analysis and Processing (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Valentina Casola
    • 2
  • Mariana Esposito
    • 1
    • 2
  • Francesco Flammini
    • 1
  • Nicola Mazzocca
    • 2
  • Concetta Pragliola
    • 1
  1. 1.Ansaldo STSNaplesItaly
  2. 2.Department of Electrical Engineering and Information TechnologyUniversity of Naples Federico IINapoliItaly

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