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User-Generated Content (UGC)/In-The-Wild Video Content Recognition

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Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13758))

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Abstract

According to Cisco, we are facing a three-fold increase in IP traffic in five years, ranging from 2017 to 2022. IP video traffic generated by users is largely related to user-generated content (UGC). Although at the beginning of UGC creation, this content was often characterised by amateur acquisition conditions and unprofessional processing, the development of widely available knowledge and affordable equipment allows one to create UGC of a quality practically indistinguishable from professional content. Since some UGC content is indistinguishable from professional content, we are not interested in all UGC content, but only in the quality that clearly differs from the professional. For this content, we use the term “in the wild” as a concept closely related to the concept of UGC, which is its special case. In this paper, we show that it is possible to deliver the new concept of an objective “in-the-wild” video content recognition model. The value of the F measure in our model is 0.988. The resulting model is trained and tested with the use of video sequence databases containing professional and “in the wild” content. These modelling results are obtained when the random forest learning method is used. However, it should be noted that the use of the more explainable decision tree learning method does not cause a significant decrease in the value of measure F (an F-measure of 0.973).

Supported by Polish National Centre for Research and Development (TANGO-IV-A/0038/2019-00).

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Notes

  1. 1.

    Video source: https://youtu.be/8GKKdnjoeH0, https://youtu.be/psKb_bSFUsU and https://youtu.be/lVuk2KXBlL8.

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Authors and Affiliations

  1. AGH University of Science and Technology, 30059, Kraków, Poland

    Mikołaj Leszczuk, Lucjan Janowski, Jakub Nawała & Michał Grega

Authors
  1. Mikołaj Leszczuk
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  2. Lucjan Janowski
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  3. Jakub Nawała
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  4. Michał Grega
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Corresponding author

Correspondence to Mikołaj Leszczuk .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Leszczuk, M., Janowski, L., Nawała, J., Grega, M. (2022). User-Generated Content (UGC)/In-The-Wild Video Content Recognition. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_29

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  • DOI: https://doi.org/10.1007/978-3-031-21967-2_29

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