Smoking Detection in Video

Authors

  • P. Sumalatha  Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • Gurram Soumya  Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • Angaluri Yashaswini Tapathi  Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, India

Keywords:

Deep Learning, YOLOv5, Quantitative Measures, Qualitative Measures.

Abstract

This paper presents a novel approach for identifying smoking behavior using deep learning to extract important features from an image. The approach involves using deep learning to identify key regions in an image and a conditional detection system built using YOLOv5 to improve performance and simplify the model. The method was tested on a dataset containing 7,000 images with equal representation of smokers and non-smokers in various settings. The effectiveness of the technique was evaluated using both quantitative and qualitative measures, resulting in a classification accuracy of 96.74% on the dataset.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-30

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
P. Sumalatha, Gurram Soumya, Angaluri Yashaswini Tapathi, " Smoking Detection in Video" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.177-182, May-June-2023.