Advertisement

Semantic Web Technologies for Object Tracking and Video Analytics

  • Benoit Gaüzère
  • Claudia Greco
  • Pierluigi RitrovatoEmail author
  • Alessia Saggese
  • Mario Vento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

As demonstrated in several research contexts, some of the best performing state of the art algorithms for object tracking integrate a traditional bottom-up approach with some knowledge of the scene and aims of the algorithm. In this paper, we propose the use of the Semantic Web technology for representing high-level knowledge describing the elements of the scene to be analysed. In particular, we demonstrate how to use the OWL ontology language to describe scene elements and their relationships together with a SPARQL based rule language to infer on the knowledge. The proof of the implemented concept prototype is able to track people even when occlusions between persons and/or objects occur, only using the bounding box dimensions, positions and directions. We also demonstrate how the Semantic Web Technology enables powerful video analytics functions for video surveillance applications.

References

  1. 1.
    Tiejun, H.: Surveillance video: the biggest big data. Comput. Now 7 (2014)Google Scholar
  2. 2.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. Comput. Surv. 38, 1–45 (2006)CrossRefGoogle Scholar
  3. 3.
    Ferryman, J., Ellis, A.L.: Performance evaluation of crowd image analysis using the PETS2009 dataset. Pattern Recogn. Lett. 44, 3–15 (2014). Pattern recognition and crowd analysisCrossRefGoogle Scholar
  4. 4.
    Lascio, R.D., Foggia, P., Percannella, G., Saggese, A., Vento, M.: A real time algorithm for people tracking using contextual reasoning. Comput. Vis. Image Underst. 117, 892–908 (2013)CrossRefGoogle Scholar
  5. 5.
    Li, L.J., Socher, R., Fei-Fei, L.: Towards total scene understanding: classification, annotation and segmentation in an automatic framework, pp. 2036–2043 (2009)Google Scholar
  6. 6.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 255–261. IEEE (1999)Google Scholar
  7. 7.
    Zhaoping, L.: Theoretical understanding of the early visual processes by data compression and data selection. Netw. Comput. Neural Syst. (Bristol, England) 17, 301–334 (2006)CrossRefGoogle Scholar
  8. 8.
    DiCarlo, J., Zoccolan, D., Rust, N.: How does the brain solve visual object recognition? Neuron 73, 415–434 (2012)CrossRefGoogle Scholar
  9. 9.
    Baader, F.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)Google Scholar
  10. 10.
    Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum. Comput. Stud. 43, 907–928 (1995)CrossRefGoogle Scholar
  11. 11.
    McGuinness, D.L., Van Harmele, F., et al.: Owl web ontology language overview. W3C Recommendation 10, 2004 (2004)Google Scholar
  12. 12.
    Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P.F., Rudolph, S.: Owl 2 web ontology language primer. W3C Recommendation 27, 1–123 (2009)Google Scholar
  13. 13.
    Gomez-Romero, J., Patricio, M.A., Garca, J., Molina, J.M.: Ontology-based context representation and reasoning for object tracking and scene interpretation in video. Expert Syst. Appl. 38, 7494–7510 (2011)CrossRefGoogle Scholar
  14. 14.
    Knublauch, H.: Spin-modeling vocabulary. W3C Member Submission 22 (2011)Google Scholar
  15. 15.
    Bloehdorn, S., Petridis, K., Saathoff, C., Simou, N., Tzouvaras, V., Avrithis, Y., Handschuh, S., Kompatsiaris, Y., Staab, S., Strintzis, M.G.: Semantic annotation of images and videos for multimedia analysis. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 592–607. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  16. 16.
    Wang, H., Liu, S., Chia, L.T.: Does ontology help in image retrieval?: a comparison between keyword, text ontology and multi-modality ontology approaches, pp. 109–112 (2006)Google Scholar
  17. 17.
    Snidaro, L., Belluz, M., Foresti, G.: Representing and recognizing complex events in surveillance applications, pp. 493–498 (2007)Google Scholar
  18. 18.
    SanMiguel, J., Martinez, J., Garcia, A.: An ontology for event detection and its application in surveillance video. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, pp. 220–225 (2009)Google Scholar
  19. 19.
    Riboni, D., Bettini, C.: Owl 2 modeling and reasoning with complex human activities. Pervasive Mobile Comput. 7, 379–395 (2011)CrossRefGoogle Scholar
  20. 20.
    Meditskos, G., Dasiopoulou, S., Efstathiou, V., Kompatsiaris, I.: SP-ACT: a hybrid framework for complex activity recognition combining owl and sparql rules, pp. 25–30 (2013)Google Scholar
  21. 21.
    Brickley, D., Miller, L.: Foaf vocabulary specification 0.98. Namespace document 9 (2012)Google Scholar
  22. 22.
    Conte, D., Foggia, P., Percannella, G., Tufano, F., Vento, M.: An experimental evaluation of foreground detection algorithms in real scenes. EURASIP J. Adv. Sig. Process. 2010, 7 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Benoit Gaüzère
    • 1
  • Claudia Greco
    • 2
  • Pierluigi Ritrovato
    • 2
    Email author
  • Alessia Saggese
    • 2
  • Mario Vento
    • 2
  1. 1.Laboratoire d’Informatique, du Traitement de l’Information et des Systmes (LITIS)Universit de RouenSaint-Étienne-du-RouvrayFrance
  2. 2.Department of Information Engineering, Electrical Engineering and Applied MathematicsUniversity of SalernoFiscianoItaly

Personalised recommendations