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A novel IPTV framework for automatic TV commercials detection, labeling, recognition and replacement

  • 1207: Innovations in Multimedia Information Processing & Retrieval
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Abstract

Advertisements are one of the most important way for companies to access their customers. In this context, televison commercials are gaining significant importance in many sectors daily, and it is crucial for companies to promote their products in the best way. This creates a big rivalry between companies. From this point of view, we have created an IPTV Framework that can automatically detect commercials of rival companies and replace them with desired commercials for companies to help them highlight their products to their customers. We have benefited from monochrome frames to detect the Livestream commercial block and proposed a fingerprint algorithm to create an automatic commercial database. We can easily recognize the commercials, and we can mask the commercials of rival companies with these techniques. We have tested our algorithm in real-time by streaming a recorded broadcast from a server of a specific TV channel. Experimental results show that our algorithm provides high accuracy in real-time commercial recognition.

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References

  1. (2017) How Consumers View Advertising: 2017 Survey. 2017. [Online]. Available at https://clutch.co/agencies/resources/how-consumers-view-advertising-survey-2017. Accessed 10 Nov 2019

  2. (2018) US Online and Traditional Media Advertising Outlook, 2018–2022. [Online]. Available at https://www.marketingcharts.com/featured-104785. Accessed 13 Nov 2019

  3. Berrani SA, Manson G, Lechat P (2008) A non-supervised approach for repeated sequence detection in TV broadcast streams. Signal Process Image Commun 23(7):525–537. https://doi.org/10.1016/j.image.2008.04.018

    Article  Google Scholar 

  4. Covell M, Baluja S, Fink M (2006) Advertisement detection and replacement using acoustic and visual repetition. 2006 IEEE 8th workshop on multimedia signal processing, MMSP 2006. IEEE, Victoria, pp 461–466. https://doi.org/10.1109/MMSP.2006.285351

    Chapter  Google Scholar 

  5. Definition of “Advertising” [Online]. Available at https://economictimes.indiatimes.com/definition/advertising. Accessed 10 Nov 2019

  6. Fati SM, Sumari P (2019) A survey on content awareness challenges in IPTV delivery networks. Multimedia Tools and Applications 78(12):16817–16842. https://doi.org/10.1007/s11042-018-7057-3

    Article  Google Scholar 

  7. Gomes A, Queluz MP, Pereira F, (2018) Automatic detection of TV commercial blocks: a new approach based on digital on-screen graphics classification. In: 2017, 11th International Conference on Signal Processing and Communication Systems, ICSPCS 2017—Proceedings, vol 2018-Janua. pp 1–6. https://doi.org/10.1109/ICSPCS.2017.8270456.

  8. Hauptmann AG, Witbrock MJ (1998) Story segmentation and detection of commercials in broadcast news video. Proceedings IEEE ınternational forum on research and technology advances in digital libraries -ADL’98-. IEEE, Santa Barbara, pp 168–179

    Chapter  Google Scholar 

  9. Hua XS, Lu L, Zhang HJ (2005) Robust learning-based TV commercial detection. 2005 IEEE ınternational conference on multimedia and expo. IEEE, Amsterdam. https://doi.org/10.1109/ICME.2005.1521382

    Chapter  Google Scholar 

  10. IMARC Group (2019) IPTV market: global industry trends, share, size, growth, opportunity and forecast 2019–2024. IMARC Group, Noida

    Google Scholar 

  11. Jiang DY, Kim J (2018) Video searching and fingerprint detection by using the image query and PlaceNet-based shot boundary detection method. Appl Sci (Switzerland) 8(10):1735. https://doi.org/10.3390/app8101735

    Article  Google Scholar 

  12. Kannao R, Guha P (2015) TV advertisement detection for news channels using local success weighted SVM ensemble. 2015 Annual IEEE India Conference (INDICON). IEEE, New Delhi, pp 1–6. https://doi.org/10.1109/INDICON.2015.7443801

    Chapter  Google Scholar 

  13. Kim HG, Cho HS, Kim JY (2015) TV advertisement search based on audio peak-pair hashing in real environments. 2015 5th ınternational conference on IT convergence and security (ICITCS). IEEE, Kuala Lumpur, pp 1–4. https://doi.org/10.1109/ICITCS.2015.7293031

    Chapter  Google Scholar 

  14. Kitanovski V, Taskovski D (2010) Real-time TV commercial monitoring based on robust visual hashing. In: 2010 2nd European workshop on visual ınformation processing (EUVIP). pp 140–143. https://doi.org/10.1109/EUVIP.2010.5699100

  15. Li Y, Zhang D, Zhou X, Jin JS (2008) A confidence based recognition system for TV commercial extraction. Proceedings of the nineteenth conference on Australasian database, vol 75. Autralian Computer Society, Inc., AUS, ADC’08, Darlinghurst, pp 57–64

    Google Scholar 

  16. Li M, Guo Y, Chen Y (2017) CNN-based commercial detection in TV broadcasting. Proceedings of the 2017 VI ınternational conference on network, communication, and computing, association for computing machinery. ICNCC, New York, pp 48–53. https://doi.org/10.1145/3171592.3171619

    Chapter  Google Scholar 

  17. Lienhart R, Kuhmuench C, Effelsberg W (1997) On the detection and recognition of television commercials. International conference on multimedia computing and systems—proceedings. IEEE, Ottawa, pp 509–516. https://doi.org/10.1109/mmcs.1997.609763

    Chapter  Google Scholar 

  18. Putpuek N, Cooharojananone N, Lursinsap C, Satoh SN (2010) Unified approach to detection and identification of commercial films by temporal occurrence pattern. Proceedings—ınternational conference on pattern recognition, June 2014. IEEE, Istanbul, pp 3288–3291. https://doi.org/10.1109/ICPR.2010.804

    Chapter  Google Scholar 

  19. Sadlier DA, Marlow S, O’Connor N, Murphy N (2002) Automatic TV advertisement detection from MPEG bitstream. Pattern Recogn 35(12):2719–2726. https://doi.org/10.1016/S0031-3203(01)00251-5

    Article  MATH  Google Scholar 

  20. Sarica S, Ince G (2017) Audio-based commercial monitoring in TV broadcasts. 2017 25th signal processing and communications applications conference (SIU). IEEE, Antalya, pp 1–4. https://doi.org/10.1109/SIU.2017.7960392

    Chapter  Google Scholar 

  21. Schöffmann K, Lux M, B¨osz¨ormenyi L, (2009) A novel approach for fast and accurate commercial detection in h.264/avc bitstreams based on logo identification. In: Huet B, Smeaton A, Mayer-Patel K, Avrithis Y (eds) Advances in multimedia modeling, vol 5371. Springer, Berlin, Heidelberg, pp 119–127. https://doi.org/10.1007/978-3-540-92892-813

    Chapter  Google Scholar 

  22. Senarath R, Pathmabandu R, Kularathna K, Ranathunga L (2017) Automated TV commercial tracking and auditing. 2017 IEEE ınternational conference on ındustrial and ınformation systems (ICIIS). IEEE, Peradeniya, pp 1–6. https://doi.org/10.1109/ICIINFS.2017.8300389

    Chapter  Google Scholar 

  23. Shivadas A, Gauch JM (2007) Real-time commercial recognition using color moments and hashing. In: Proceedings—fourth Canadian conference on computer and robot vision, CRV. pp 465–472. https://doi.org/10.1109/CRV.2007.53

  24. Tapu R, Mocanu B, Zaharia T (2020) DEEP-AD: a multimodal temporal video segmentation framework for online video advertising. IEEE Access 8:99582–99597. https://doi.org/10.1109/ACCESS.2020.2997949

    Article  Google Scholar 

  25. Tvheadend. https://tvheadend.org/. Accessed 18 Oct 2019

  26. Wen X, Huffmire TD, Hu HH, Finkelstein A (1999) Wavelet-based video indexing and querying. Multimedia Syst 7(5):350–358. https://doi.org/10.1007/s005300050137

    Article  Google Scholar 

  27. Wu X, Satoh S (2013) Ultrahigh-speed TV commercial detection, extraction, and matching. IEEE Trans Circuits Syst Video Technol 23(6):1054–1069. https://doi.org/10.1109/TCSVT.2013.2248991

    Article  Google Scholar 

  28. Zhang B, Li T, Ding P, Xu B (2012) Tv commercial detection using audio-visual features and support vector machine. 2012 ınternational symposium on ınstrumentation measurement, sensor network and automation (IMSNA), vol 1. IEEE, Sanya, pp 322–325

    Chapter  Google Scholar 

  29. Zhang B, Feng B, Ding P, Xu B (2012) TV commercial detection using constrained viterbi algorithm based on time distribution. 2012 9th ınternational conference on fuzzy systems and knowledge discovery. IEEE, Chongqing, pp 2010–2014. https://doi.org/10.1109/FSKD.2012.6234003

    Chapter  Google Scholar 

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Acknowledgements

This project was financially supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK). The Project Grant Number is 7160967.

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Author notes

  1. Enes Emre Bulut, Berk Eren, Ahmet Uzgor and Selcuk Yolcu have contributed equally to this work.

Authors and Affiliations

  1. Computer Engineering Department, Kadir Has University, Istanbul, 34083, Turkey

    Taner Arsan

  2. Turcom Technology, Istanbul, 34485, Turkey

    Enes Emre Bulut, Berk Eren, Ahmet Uzgor & Selcuk Yolcu

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  1. Taner Arsan
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Correspondence to Taner Arsan.

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Arsan, T., Bulut, E.E., Eren, B. et al. A novel IPTV framework for automatic TV commercials detection, labeling, recognition and replacement. Multimed Tools Appl 82, 8561–8579 (2023). https://doi.org/10.1007/s11042-021-11563-y

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