Vision Based Driver Smoking Behavior Detection Using Surveillance Camera Images

Artan, Yusuf; Balcı, Burak; Elihoş, Alperen; Alkan, Bensu

doi:10.1007/978-3-030-30645-8_43

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…The street-side officials conduct physical smoking tests daily. The study (Artan et al 2019) suggests a robotic approach to perform the location of driver smoking using near-infrared (NIR) recognition camera images. Cigarette tips arrive at 800-900 C during the puff creating a problem area on the NIR images.…”

Section: Related Workmentioning

confidence: 99%

Deep CNN-based autonomous system for safety measures in logistics transportation

et al. 2021

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Related Workmentioning

confidence: 99%

Deep CNN-based autonomous system for safety measures in logistics transportation

et al. 2021

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“…It was feasible to determine the occurrence of smoking by selecting a smoke area [5]. Using near-infrared (NIR) security camera images, the authors in [6] presented an automated method for detection driver smoking behaviour. However, the camerabased techniques necessitate the recording of the target, which poses severe privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

RF Sensing for Smoking Detection at Oil Fields

Hameed

Azam

Usman

et al. 2022

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)

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In this paper, an Ultra-wideband (UWB) Radar sensor is used to detect human gestures while smoking or vaping in potentially dangerous areas such as an oil field or a gas station. Existing smoking detection systems are primarily camera-based, which has a number of drawbacks, including poor illumination, training issues with longer video sequence data, and major privacy concerns. The data collected from a UWB Radar is represented in the form of spectrograms. Three classes are considered, namely cigarette, vape and when the subject is not smoking. InceptionV3, VGG19, and VGG16 deep learning algorithms are used to extract spatiotemporal information from the Spectrogram. Finally, by classifying the Spectrograms into the considered gestures, the smoking and/or vaping is accurately identified. The simulation results show that InceptionV3 can achieve a maximum classification accuracy of 90.00%.

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