CNN-Based Smoker Classification and Detection in Smart City Application

To better regulate smoking in no-smoking areas, we present a novel AI-based surveillance system for smart cities. In this paper, we intend to solve the issue of no-smoking area surveillance by introducing a framework for an AI-based smoker detection system for no-smoking areas in a smart city. Moreover, this research will provide a dataset for smoker detection problems in indoor and outdoor environments to help future research on this AI-based smoker detection system. The newly curated smoker detection image dataset consists of two classes, Smoking and NotSmoking. Further, to classify the Smoking and NotSmoking images, we have proposed a transfer learning-based solution using the pre-trained InceptionResNetV2 model. The performance of the proposed approach for predicting smokers and not-smokers was evaluated and compared with other CNN methods on different performance metrics. The proposed approach achieved an accuracy of 96.87% with 97.32% precision and 96.46% recall in predicting the Smoking and NotSmoking images on a challenging and diverse newly-created dataset. Although, we trained the proposed method on the image dataset, we believe the performance of the system will not be affected in real-time.

Automated summary

This paper presents an AI-based surveillance system for smart cities to regulate smoking in no-smoking areas. The research introduces a framework for an AI-based smoker detection system and provides a dataset for future research on smoker detection. The proposed transfer learning-based solution using the InceptionResNetV2 model achieves high accuracy, precision, and recall in predicting smoking and non-smoking images on a newly-created dataset.