A UAV-Driven Surveillance System to Support Rescue Intervention

  • Danilo Cavaliere
  • Vincenzo Loia
  • Sabrina SenatoreEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11184)


In recent years, the intelligent surveillance systems have attracted many application domains, due to the increasing demand on security and safety. Unmanned Areal Vehicles (AUVs) represent the reliable, low-cost solution for mobile sensor node deployment, localization, and collection of measurements.

This paper presents a surveillance UAV-based system, aimed at understanding the scene situation by collecting raw data from the environment (by exploiting some possible sensor modalities: CCTV camera, infrared camera, thermal camera, radar, etc.), processing their fusion and yielding a semantic, high-level scenario description. UAV is able to recognize objects and the spatio-temporal relations with other objects and the environment. Moreover, UAV is able to individuate alerting situations and suggest a recommended intervention to humans. A Fuzzy cognitive map model is indeed, injected in the UAV: from the semantic description of the scenario, the UAV is able to deduct casual effect of occurring situations, that enhances the scenario understanding, especially when alarming situations are discovered.


Situation understanding Situation awareness Fuzzy cognitive maps Semantic Web 


  1. 1.
    Bernad, J., Bobed, C., Mena, E., Ilarri, S.: A formalization for semantic location granules. Int. J. Geogr. Inf. Sci. 27(6), 1090–1108 (2013). Scholar
  2. 2.
    Bobed, C., Ilarri, S., Mena, E.: Exploiting the semantics of location granules in location-dependent queries. In: Catania, B., Ivanović, M., Thalheim, B. (eds.) ADBIS 2010. LNCS, vol. 6295, pp. 73–87. Springer, Heidelberg (2010). Scholar
  3. 3.
    Cavaliere, D., Loia, V., Saggese, A., Senatore, S., Vento, M.: Semantically enhanced UAVs to increase the aerial scene understanding. IEEE Trans. Syst. Man Cybern. Syst. PP(99), 1–13 (2017). Scholar
  4. 4.
    Chauvin, L., Genest, D., Loiseau, S.: Ontological cognitive map. In: 2008 20th IEEE International Conference on Tools with Artificial Intelligence, vol. 2, pp. 225–232, November 2008.
  5. 5.
    Crispim-Junior, C.F., et al.: Semantic event fusion of different visual modality concepts for activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. 38(8), 1598–1611 (2016). Scholar
  6. 6.
    D’Aniello, G., Gaeta, M., Hong, T.P.: Effective quality-aware sensor data management. IEEE Trans. Emerg. Topics Comput. Intell. 2(1), 65–77 (2018). Scholar
  7. 7.
    Glykas, M.: Fuzzy cognitive strategic maps in business process performance measurement. Expert Syst. Appl. 40(1), 1–14 (2013). Scholar
  8. 8.
    Glykas, M.: Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications, 1st edn. Springer, Heidelberg (2010). Scholar
  9. 9.
    Gómez-Romero, J., Patricio, M.A., García, J., Molina, J.M.: Ontology-based context representation and reasoning for object tracking and scene interpretation in video. Expert Syst. Appl. 38(6), 7494–7510 (2011). Scholar
  10. 10.
    Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24(1), 65–75 (1986). Scholar
  11. 11.
    Lee, D.H., Lee, H.: Construction of holistic fuzzy cognitive maps using ontology matching method. Expert Syst. Appl. 42(14), 5954–5962 (2015). Scholar
  12. 12.
    Lee, H., Kwon, S.J.: Ontological semantic inference based on cognitive map. Expert Syst. Appl. 41(6), 2981–2988 (2014). Scholar
  13. 13.
    Li, X., Lu, H.: Object tracking based on local learning. In: 2012 19th IEEE International Conference on Image Processing, pp. 413–416, September 2012.
  14. 14.
    Li, Y., Guo, Y., Kao, Y., He, R.: Image piece learning for weakly supervised semantic segmentation. IEEE Trans. Syst. Man Cybern. Syst. 47(4), 648–659 (2017). Scholar
  15. 15.
    Meditskos, G., Kompatsiaris, I.: iknow: ontology-driven situational awareness for the recognition of activities of daily living. Pervasive Mob. Comput. 40, 17–41 (2017). Scholar
  16. 16.
    Min, W., Zhang, Y., Li, J., Xu, S.: Recognition of pedestrian activity based on dropped-object detection. Sig. Process. 144, 238–252 (2018). Scholar
  17. 17.
    Piccardi, M.: Background subtraction techniques: a review. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), vol. 4, pp. 3099–3104 vol. 4, October 2004.
  18. 18.
    Rangel, J.C., Martínez-Gómez, J., Romero-González, C., García-Varea, I., Cazorla, M.: Semi-supervised 3D object recognition through CNN labeling. Appl. Soft Comput. 65, 603–613 (2018). Scholar
  19. 19.
    Smedt, F.D., Hulens, D., Goedemé, T.: On-board real-time tracking of pedestrians on a UAV. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–8, June 2015.
  20. 20.
    Snidaro, L., García, J., Llinas, J.: Context-based information fusion: a survey and discussion. Inf. Fusion 25, 16–31 (2015). Scholar
  21. 21.
    Yuan, Y., Mou, L., Lu, X.: Scene recognition by manifold regularized deep learning architecture. IEEE Trans. Neural Netw. Learn. Syst. 26(10), 2222–2233 (2015). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria dell’Informazione ed Elettrica e Matematica ApplicataUniversitá degli Studi di SalernoFiscianoItaly
  2. 2.Dipartimento di Scienze Aziendali, Management e Innovation SystemsUniversitá degli Studi di SalernoFiscianoItaly

Personalised recommendations