Region Extraction Based Approach for Cigarette Usage Classification Using Deep Learning

Anshul Pundhir, Deepak Verma, Puneet Kumar, Balasubramanian Raman

Abstract: 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 conditionally active detection module based on Yolo-v3, which improves the model’s performance and reduces its complexity. To the best of our knowledge, we are the first to work on the dataset named “Dataset containing smoking and not-smoking images (smoker vs. non-smoker)”. 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.