Region extraction based approach for cigarette usage classification using deep learning
This work addresses cigarette usage classification for public health or surveillance applications, but it is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled the problem of classifying smoking behavior from images by extracting relevant regions using deep learning, achieving a classification accuracy of 96.74% on a dataset of 2,400 images.
This paper has proposed a novel approach to classify the subjects' smoking behavior by extracting relevant regions from a given image using deep learning. After the classification, we have proposed a conditional detection module based on Yolo-v3, which improves model's performance and reduces its complexity. As per the best of our knowledge, we are the first to work on this dataset. This dataset contains a total of 2,400 images that include smokers and non-smokers equally in various environmental settings. We have evaluated the proposed approach's performance using quantitative and qualitative measures, which confirms its effectiveness in challenging situations. The proposed approach has achieved a classification accuracy of 96.74% on this dataset.