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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases pp 663–667Cite as

An Advert Creation System for Next-Gen Publicity

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11053)

Abstract

With the rapid proliferation of multimedia data in the internet, there has been a fast rise in the creation of videos for the viewers. This enables the viewers to skip the advertisement breaks in the videos, using ad blockers and ‘skip ad’ buttons – bringing online marketing and publicity to a stall. In this paper, we demonstrate a system that can effectively integrate a new advertisement into a video sequence. We use state-of-the-art techniques from deep learning and computational photogrammetry, for effective detection of existing adverts, and seamless integration of new adverts into video sequences. This is helpful for targeted advertisement, paving the path for next-gen publicity. Code related to this paper is available at: https://youtu.be/zaKpJZhBVL4.

Keywords

  • Advertisement
  • Online content
  • Deep learning

A. Nautiyal, K. McCabe, M. Hossari and S. Dev—Contributed equally and arranged alphabetically.

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Notes

  1. 1.

    In this paper, we interchangeably use both the terms, billboard and advert to indicate a candidate object for new advertisement integration in an image frame.

  2. 2.

    A demonstration video of our advert creation system can be accessed via https://youtu.be/zaKpJZhBVL4.

References

  1. Mirriad: Scalable, effective campaigns (2018). http://www.mirriad.com/. Accessed 7 May 2018

  2. Lucas, B.D., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision (1981)

    Google Scholar 

  3. Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph. (TOG) 22(3), 313–318 (2003)

    CrossRef  Google Scholar 

  4. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)

    Google Scholar 

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Acknowledgement

The ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

Author information

Authors and Affiliations

  1. The ADAPT SFI Research Centre, Trinity College Dublin, Dublin, Republic of Ireland

    Atul Nautiyal, Killian McCabe, Murhaf Hossari, Soumyabrata Dev, Matthew Nicholson, Clare Conran, Declan McKibben & François Pitié

  2. Department of Electronic and Electrical Engineering, Trinity College Dublin, Dublin, Republic of Ireland

    François Pitié

  3. Huawei Ireland Research Center, Dublin, Republic of Ireland

    Jian Tang & Wei Xu

Authors
  1. Atul Nautiyal
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  2. Killian McCabe
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  3. Murhaf Hossari
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  4. Soumyabrata Dev
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  5. Matthew Nicholson
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  6. Clare Conran
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  7. Declan McKibben
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  8. Jian Tang
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  9. Wei Xu
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  10. François Pitié
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Corresponding author

Correspondence to Soumyabrata Dev .

Editor information

Editors and Affiliations

  1. Leuphana University, Lüneburg, Germany

    Prof. Dr. Ulf Brefeld

  2. National University of Ireland, Galway, Ireland

    Edward Curry

  3. IBM Research - Ireland, Dublin, Ireland

    Elizabeth Daly

  4. University College Dublin, Dublin, Ireland

    Dr. Brian MacNamee

  5. Nokia (Ireland), Dublin, Ireland

    Alice Marascu

  6. Vodafone, Milan, Italy

    Fabio Pinelli

  7. IBM Research - Ireland, Dublin, Ireland

    Michele Berlingerio

  8. University College Dublin, Dublin, Ireland

    Neil Hurley

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Nautiyal, A. et al. (2019). An Advert Creation System for Next-Gen Publicity. In: , et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018. Lecture Notes in Computer Science(), vol 11053. Springer, Cham. https://doi.org/10.1007/978-3-030-10997-4_47

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  • DOI: https://doi.org/10.1007/978-3-030-10997-4_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-10996-7

  • Online ISBN: 978-3-030-10997-4

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