Recognizing Context for Privacy Preserving of First Person Vision Image Sequences

  • Sebastiano Battiato
  • Giovanni Maria FarinellaEmail author
  • Christian Napoli
  • Gabriele Nicotra
  • Salvatore Riccobene
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)


The constant increasing evolution of life-logging wearable devices, as well as the fast grow of their market, has introduced relevant changes in the acquisition, storage and automatic understanding of images and videos. Along with the novel users’ opportunities, this technology is introducing a large amount of privacy-related concerns, mainly regarding the unaware or unwilling contexts subject that could get recorded by a life-logging device. In this work, we devise an approach to help life-logging wearable devices enforcing restrictions for context-related users’ privacy preservation. The proposed approach joins different technological innovations, from computer vision techniques to bluetooth beacon technology and network security.


  1. 1.
    Mann, S.: Wearable computing: a first step toward personal imaging. Computer 30(2), 25–32 (1997)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Cheng, W.C., Golubchik, L., Kay, D.G.: Total recall: are privacy changes inevitable? In: Proceedings of the 1st ACM Workshop on Continuous Archival and Retrieval of Personal Experiences, pp. 86–92. ACM (2004)Google Scholar
  3. 3.
    Allen, A.L.: Dredging up the past: lifelogging, memory, and surveillance. Univ. Chicago Law Rev. 75(1), 47–74 (2008)Google Scholar
  4. 4.
    Chen, Y., Jones, G.J.: Augmenting human memory using personal lifelogs. In: Proceedings of the 1st Augmented Human International Conference, p. 24. ACM (2010)Google Scholar
  5. 5.
    Ortis, A., Farinella, G.M., D’Amico, V., Addesso, L., Torrisi, G., Battiato, S.: Organizing egocentric videos for daily living monitoring. In: Proceedings of the First Workshop on Lifelogging Tools and Applications, pp. 45–54. ACM (2016)Google Scholar
  6. 6.
    Furnari, Antonino, Farinella, Giovanni Maria, Battiato, Sebastiano: Temporal segmentation of egocentric videos to highlight personal locations of interest. In: Hua, Gang, Jégou, Hervé (eds.) ECCV 2016. LNCS, vol. 9913, pp. 474–489. Springer, Cham (2016). doi: 10.1007/978-3-319-46604-0_34 CrossRefGoogle Scholar
  7. 7.
    Teraoka, T.: Organization and exploration of heterogeneous personal data collected in daily life. Hum.-Centric Comput. Inf. Sci. 2(1), 1 (2012)CrossRefGoogle Scholar
  8. 8.
    Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)Google Scholar
  9. 9.
    Templeman, R., Rahman, Z., Crandall, D., Kapadia, A.: Placeraider: virtual theft in physical spaces with smartphones. arXiv preprint arXiv:1209.5982 (2012)
  10. 10.
    Ryoo, M.S., Rothrock, B., Fleming, C.: Privacy-preserving egocentric activity recognition from extreme low resolution. arXiv preprint arXiv:1604.03196 (2016)
  11. 11.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, Heidelberg (2011). doi: 10.1007/978-1-84882-935-0 CrossRefzbMATHGoogle Scholar
  12. 12.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks, pp. 1097–1105 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sebastiano Battiato
    • 1
  • Giovanni Maria Farinella
    • 1
    Email author
  • Christian Napoli
    • 1
  • Gabriele Nicotra
    • 1
  • Salvatore Riccobene
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly

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