| Title |
CNN-Based Smoker Classification and Detection in Smart City Application |
| ID_Doc |
38523 |
| Authors |
Khan, AL; Khan, S; Hassan, B; Zheng, ZL |
| Title |
CNN-Based Smoker Classification and Detection in Smart City Application |
| Year |
2022 |
| Published |
Sensors, 22, 3 |
| DOI |
10.3390/s22030892 |
| Abstract |
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. |
| Author Keywords |
AI-based surveillance; smoker detection dataset; smoker classification; transfer learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000754662600001 |
| WoS Category |
Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation |
| Research Area |
Chemistry; Engineering; Instruments & Instrumentation |
| PDF |
https://www.mdpi.com/1424-8220/22/3/892/pdf?version=1643098650
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