GlassNage: Layout Recognition for Dynamic Content Retrieval in Multi-Section Digital Signage

  • Adiyan MujibiyaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9189)


We report our approach to support dynamic content transfer from publicly available large display digital signage to users’ private display, specifically Glass-like wearable devices. We aim to address issues concerning dynamic multimedia signage where the content are divided into several sections. This type of signage has become increasingly popular due to optimal content exposures. In contrast to prior research, our approach excludes computer vision based object recognition, and instead took an approach to identify how contents are being laid-out in a digital signage. We incorporate techniques to recognize basic layout features including corners, lines, edges, and line segments; which are obtained from the camera frame taken by the user using their own device. Consequently, these layout features are combined to generate signage layout map, which is then compared to pre-learned layout map for position detection and perspective correction using homography estimation. To grab a specific content, users are able to choose a section within the captured layout using the device’s interface, which in turn creates a request to contents server to send respective content information based on a timestamp and a unique section ID. In this paper, we describe implementation details, report user study results, and conclude with discussion of our experiences in implementation as well as highlighting future work.


Digital signage Public display Public-to-private Multi section Layout recognition Computer vision Visual features Line segment User study 


  1. 1.
    She, J., Crowcroft, J., Fu, H., Li, F.: Convergence of interactive displays with smart mobile devices for effective advertising: a survey. ACM Trans. Multimedia Comput. Commun. Appl. 10(2), Article 17, 16 pp. (2014) doi: 10.1145/2557450
  2. 2.
    Turner, J.: Cross-device eye-based interaction. In: Proceedings of the Adjunct Publication of the 26th Annual ACM Symposium on User Interface Software and Technology (UIST 2013 Adjunct), pp. 37–40. ACM, New York, NY, USA (2013). doi: 10.1145/2508468.2508471
  3. 3.
    von Gioi, R.G., Jakubowicz, J., Morel, J.-M., Randall, G.: LSD: a line segment detector. Image Process. Line 2(2012), 35–55 (2012). CrossRefGoogle Scholar
  4. 4.
    Boring, S., Baur, D., Butz, A., Gustafson, S., Baudisch, P.: Touch projector: mobile interaction through video. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2010), pp. 2287–2296. ACM, New York, NY, USA (2010). doi: 10.1145/1753326.1753671
  5. 5.
    Boring, S., Altendorfer, M., Broll, G., Hilliges, O., Butz, A.: Shoot and copy: phonecam-based information transfer from public displays onto mobile phones. In: Proceedings of the 4th International Conference on Mobile Technology, Applications, and Systems and the 1st International Symposium on Computer Human Interaction in Mobile Technology (Mobility 2007), pp. 24–31. ACM, New York, NY, USA (2007). doi: 10.1145/1378063.1378068
  6. 6.
    Boring, S., Gehring, S., Wiethoff, A., Blöckner, A.M., Schöning, J., Butz, A.: Multi-user interaction on media facades through live video on mobile devices. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2011), pp. 2721–2724. ACM, New York, NY, USA (2011). doi: 10.1145/1978942.1979342
  7. 7.
    The QR Code Tesco Store: From Concept to Reality. Accessed 20 Feb 2015
  8. 8.
    Yamaguchi, T., Fukushima, H., Tatsuzawa, S., Nonaka, M., Takashima, K., Kitamura, Y.: SWINGNAGE: gesture-based mobile interactions on distant public displays. In: Proceedings of the 2013 ACM International Conference on Interactive Tabletops and Surfaces (ITS 2013), pp. 329–332 (2013). ACM, New York, NY, USA. doi: 10.1145/2512349.2514596
  9. 9.
    Chu, H.-K., Chang, C.-S., Lee, R.-R., Mitra, N.J.: Halftone QR codes. ACM Trans. Graph. Article 217 32(6), 8 pp. (2013). doi: 10.1145/2508363.2508408
  10. 10.
    Muja, M., Lowe, D.: Fast approximate nearest neighbors with automatic algorithm configuration. In: Proceedings of the International Conference on Computer Vision Theory and Applications, pp. 331–340 (2009)Google Scholar
  11. 11.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183 (1988). North-Holland, ISSN 0166-4115, ISBN 9780444703880CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Rakuten Institute of Technology, Rakuten Inc.Shinagawa-Ku, TokyoJapan

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