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Artificial Intelligence Based Emergency Identification Computer System

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The use of Artificial Intelligence is currently being observed in many areas of life. In addition to assisting in intellectual work, solving complex computational problems, or analyzing various types of data, the aforementioned techniques can also be applied in the process of providing security to people. The paper proposes an emergency identification system based on Artificial Intelligence that aims to provide timely detection and notification of dangerous situations. The proposed solution consider the position of a person "hands up" as an emergency situation that will indicate a potential danger for a person. Because people in the face of potential danger are mostly forced to raise their hands up and this pose attracts attention, emphasizes the emotional reaction to certain events and is usually used as a sign of risk or as a means of subjugation. The system should recognize the pose of a person, detect it, and consequently inform about the threat. In this paper, an AI based emergency identification system was proposed to detect the human pose "hands up" for emergency identification using the PoseNet Machine Learning Model. The assumption consists that the utilization only of 6 key points made allows reducing the computing resources of the system since the conclusion is made taking into account a smaller amount of data. For the study, a dataset of 1510 images was created for training an Artificial Intelligence model, and the decisions were verified. Supervised Machine Learning methods are used to classify the definition of an emergency. Alternative methods: Support Vector Machine, Logistic Regression, Naïve Bayes Classifier, Discriminant Analysis Classifier, and K-nearest Neighbours Classifier based on the accuracy were evaluated. Overall, the paper presents a comprehensive and innovative approach to emergency identification for quick response to them using the proposed system.
Twórcy
  • Computer Systems and Networks Department, Faculty of Computer Information Systems and Software Engineering, Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine
  • Computer Systems and Networks Department, Faculty of Computer Information Systems and Software Engineering, Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine
  • Department of Radio Engineering Systems, Faculty of Applied Information Technologies and Electrical Engineering, Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine
  • Computer Systems and Networks Department, Faculty of Computer Information Systems and Software Engineering, Ternopil Ivan Puluj National Technical University, Ternopil, Ukraine
  • Department of Information Technology, Faculty of Mathematics and Information Technology, Lublin University of Technology
Bibliografia
  • 1. Park S.Y., Kwon E., Byon S., Shin W., Jung E., Lee Y. Danger detection technology based on multimodal and multilog data for public safety services. Etri Journal 2021; 44(2): 300–312. https://doi.org/10.4218/etrij.2020-0372
  • 2. Lopes N.V., Santos H., Azevedo A.I. Detection of dangerous situations using a smart internet of things system. In: Rocha A., Correia A., Costanzo S., Reis L. (eds) New contributions in information systems and technologies. Advances in Intelligent Systems and Computing, vol. 354. Springer, Cham, 2015. https://doi.org/10.1007/978-3-319-16528-8_36
  • 3. Jang S., Battulga L., Nasridinov A. Detection of dangerous situations using deep learning model with relational inference. Journal of Multimedia Information System 2020; 7(3): 205–214. https://doi.org/10.33851/jmis.2020.7.3.205
  • 4. Prati A. An intelligent surveillance system for dangerous situation detection in home environments. Unipr, 2016. https://www.academia.edu/21940139/An_Intelligent_Surveillance_System_for_Dangerous_Situation_Detection_in_Home_Environments
  • 5. Yadav, A., Thaker, N., Makwana, D., Waingankar, N., Upadhyay, P. Intruder Detection System: A Literature Review 2021; Proceedings of the 4th International Conference on Advances in Science & Technology (ICAST2021), Available at SSRN: http://dx.doi.org/10.2139/ssrn.3866777
  • 6. Dang Q., Yin J., Wang B., Zheng W. Deep learning based 2D human pose estimation: A survey. Tsinghua Science & Technology 2019; 24(6): 663–676. https://doi.org/10.26599/tst.2018.9010100
  • 7. Gong W., Zhang X., Gonzàlez J., Sobral A., Bouwmans T., Tu C., Zahzah E. Human pose estimation from monocular images: a comprehensive survey. Sensors 2016; 16(12): 1966. https://doi.org/10.3390/s16121966
  • 8. Odemakinde E. Human Pose Estimation with Deep Learning – Ultimate Overview in 2023. Viso.ai, 2023, https://viso.ai/deep-learning/pose-estimation-ultimate-overview/
  • 9. Toshpulatov M., Lee W., Lee S., Roudsari A.H. Human Pose, Hand., Mesh Estimation using Deep Learning: a Survey. The Journal of Supercomputing 2022; 78(6): 7616–7654. https://doi.org/10.1007/s11227-021-04184-7
  • 10. University of Toronto Machine Intelligence Team. Sparse R-CNN: towards more efficient object Detection models. Medium, 2021, https://utorontomist.medium.com/sparse-r-cnn-towards-more-efficient-object-detection-models-fb244178998f
  • 11. Pandey S. Posenet model in ML. OpenGenus IQ: Computing Expertise & Legacy, 2022, https://iq.opengenus.org/posenet-model/
  • 12. Hastie, T., Tibshirani, R., Friedman, J.H. Data Mining, Inference, and Prediction, Second Edition. Springer, New York; 2009, https://doi.org/10.1007/978-0-387-84858-7
  • 13. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006; 27(8): 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
  • 14. Machine Learning | Google for Developers. (n.d.). Google for Developers. https://developers.google.com/machine-learning/crash-course
  • 15. Alyamani A., Yasniy O. Classification of EEG signal by methods of machine learning. Applied Computer Science 2020; 16(4): 56–63. doi:10.23743/acs-2020-29
  • 16. Shabliy N., Lupenko S., Lutsyk N., Yasniy O., Malyshevska O. Keystroke Dynamics Analysis using Machine Learning Methods. Applied Computer Science 2021; 17(4): 75–83. https://doi.org/10.23743/acs-2021-30.
  • 17. Yasniy O., Lutsyk N., Demchyk V., Osukhivska H., Malyshevska O. The prediction of structural properties of Ni-Ti shape memory alloy by the supervised machine learning methods. ITTAP 2023: 73–78. https://ceur-ws.org/Vol-3628/short1.pdf
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8bc73731-2004-4c36-a606-de6b16db1236
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