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The Impact of Image Resolution in the training of Generative Adversarial Networks for Violence Detection

Published:20 April 2023Publication History

ABSTRACT

Since time immemorial, violence has been a problem that the world has been facing. The rise of technology has presented an opportunity to help in this matter. Violence detection solutions have been created for this cause. The problem with existing solutions is that they are not appropriate for settings in a developing country. Factors such as the place, objects seen, people involved, among others, are different from those models who are trained with datasets from developed countries, which might prove ineffective for developing countries. That is why the researchers aim to create a Generative Adversarial Networks Model trained with data that are location-specific to the country of Philippines. In this study, the researchers will gauge the effects and impact that resolution brings in the training of the GAN Model, named V.GAN, to help with improving its performance and implementation.

References

  1. [1] 6Wresearch. 2020. Philippines Video Surveillance Market (2020-2026). https://www.6wresearch.com/industry-report/philippines-video-surveillance-market-2020-2026Google ScholarGoogle Scholar
  2. [2] Al-Maamoon R. Abdali and Rana F. Al-Tuma. 2019. Robust Real-Time Violence Detection in Video Using CNN And LSTM., 104–108 pages. https://doi.org/10.1109/SCCS.2019.8852616Google ScholarGoogle Scholar
  3. [3] AIMultiple. 2021. Data Annotation: What it is & why does it matter?https://research.aimultiple.com/data-annotation/Google ScholarGoogle Scholar
  4. [4] Francis Baek, Somin Park, and Hyoungkwan Kim. 2019. Data Augmentation Using Adversarial Training for Construction-Equipment Classification. (2019). https://doi.org/10.48550/arXiv.1911.11916Google ScholarGoogle Scholar
  5. [5] David Choqueluque-Roman and Guillermo Camara-Chavez. 2022. Weakly Supervised Violence Detection in Surveillance Video. Sensors 22 (06 2022), 4502. https://doi.org/10.3390/s22124502Google ScholarGoogle Scholar
  6. [6] COE. n.d. Physical violence. https://www.coe.int/en/web/gender-matters/physical-violenceGoogle ScholarGoogle Scholar
  7. [7] Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A. Bharath. 2018. Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine 35 (01 2018), 53–65. https://doi.org/10.1109/msp.2017.2765202Google ScholarGoogle Scholar
  8. [8] FFMPEG. n.d. ffmpeg Documentation. https://ffmpeg.org/ffmpeg.htmlGoogle ScholarGoogle Scholar
  9. [9] Sumit Gupta. 2019. High Accuracy & Faster Deep Learning with High Resolution Images & Large Models. https://sumitgup.medium.com/deep-learning-with-high-resolution-images-large-models-44bfd90482a8Google ScholarGoogle Scholar
  10. [10] Enrique Bermejo Nievas, Oscar Deniz Suarez, Gloria Bueno Garcia, and Rahul Sukthankar. 2011. Violence Detection in Video Using Computer Vision Techniques. (2011).Google ScholarGoogle Scholar
  11. [11] Aravind Ramalingam. 2021. How to Pick the Optimal Image Size for Training Convolution Neural Network?https://medium.com/analytics-vidhya/how-to-pick-the-optimal-image-size-for-training-convolution-neural-network-65702b880f05Google ScholarGoogle Scholar
  12. [12] Muhammad Ramzan, Adnan Abid, Hikmat Ullah Khan, Shahid Mahmood Awan, Amina Ismail, Muzamil Ahmed, Mahwish Ilyas, and Ahsan Mahmood. 2019. A Review on State-of-the-Art Violence Detection Techniques. IEEE Access 7 (2019), 107560–107575. https://doi.org/10.1109/access.2019.2932114Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Mohamed Mostafa Soliman, Mohamed Hussein Kamal, Mina Abd El-Massih Nashed, Youssef Mohamed Mostafa, Bassel Safwat Chawky, and Dina Khattab. 2019. Violence Recognition from Videos using Deep Learning Techniques., 80–85 pages. https://doi.org/10.1109/ICICIS46948.2019.9014714Google ScholarGoogle Scholar
  14. [14] Noah Sulman, Thomas Sanocki, Dmitry Goldgof, and Rangachar Kasturi. 2008. How effective is human video surveillance performance?2008 19th International Conference on Pattern Recognition (12 2008). https://doi.org/10.1109/icpr.2008.4761655Google ScholarGoogle Scholar
  15. [15] Laura Mazzuca Toops. 2022. State of the Market: Video Surveillance | SDM Magazine. https://www.sdmmag.com/articles/100416-state-of-the-market-video-surveillanceGoogle ScholarGoogle Scholar
  16. [16] Andrew Roth Stephanie Kirchgaessner in Washington, Daniel Boffey in Brussels, Oliver Holmes in Jerusalem, and Helen Davidson in Sydney. 2020. Growth in surveillance may be hard to scale back after pandemic, experts say. The Guardian (04 2020). https://www.theguardian.com/world/2020/apr/14/growth-in-surveillance-may-be-hard-to-scale-back-after-coronavirus-pandemic-experts-sayGoogle ScholarGoogle Scholar
  17. [17] Tao Zhang, Zhijie Yang, Wenjing Jia, Baoqing Yang, Jie Yang, and Xiangjian He. 2015. A new method for violence detection in surveillance scenes. Multimedia Tools and Applications 75 (05 2015), 7327–7349. https://doi.org/10.1007/s11042-015-2648-8Google ScholarGoogle ScholarDigital LibraryDigital Library

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      AICCC '22: Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference
      December 2022
      302 pages
      ISBN:9781450398749
      DOI:10.1145/3582099

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      • Published: 20 April 2023

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