Region Extraction Based Approach for Cigarette Usage Classification Using Deep Learning

Pundhir, Anshul; Verma, Deepak; Kumar, Puneet; Raman, Balasubramanian

doi:10.1007/978-3-031-11349-9_33

Cited by 4 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literatures [31][32][33] detected two aspects of smoking gestures and cigarettes. Pundhir et al [31] classified the candidate regions after extracting the face and gesture regions, and used YOLOv3 to detect cigarettes, and the classification accuracy reached 96.74% on the specified dataset. Zhang et al [32] used YOLO object detection and AlphaPose key points detection methods to detect smoking gestures combined with graphic textures.…”

Section: Related Workmentioning

confidence: 99%

“…Pundhir et al. [31] classified the candidate regions after extracting the face and gesture regions, and used YOLOv3 to detect cigarettes, and the classification accuracy reached 96.74% on the specified dataset. Zhang et al.…”

Section: Related Workmentioning

confidence: 99%

“…Detection of smoking posture is often combined with detection of cigarettes. The literatures [31][32][33] detected two aspects of smoking gestures and cigarettes. Pundhir et al [31] classified the candidate regions after extracting the face and gesture regions, and used YOLOv3 to detect cigarettes, and the classification accuracy reached 96.74% on the specified dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Detection of smoking posture is often combined with detection of cigarettes. The literatures [31–33] detected two aspects of smoking gestures and cigarettes. Pundhir et al.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Human elbow flexion behaviour recognition based on posture estimation in complex scenes

Gong

Yuan

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Aiming at the difficulty of recognising the smoking and making phone calls behaviours of people in the complex background of construction sites, a method of recognising human elbow flexion behaviour based on posture estimation is proposed. The human upper body key points needed are retrained based on AlphaPose to achieve human object localization and key points detection. Then, a mathematical model for human elbow flexion behaviour discrimination (HEFBD model) is proposed based on human key points, as well as locating the region of interest for small object detection and reducing the interference of complex background. A super‐resolution image reconstruction method is used for pre‐processing some blurred images. In addition, YOLOv5s is improved by adding a small object detection layer and integrating a convolutional block attention model to improve the detection performance. The detection precision of this method is improved by 5.6%, and the false detection rate caused by complex background is reduced by 13%, which outperforms other state‐of‐the‐art detection methods and meets the requirement of real‐time performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Detection of smoking posture is often combined with detection of cigarettes. The literatures [31–33] detected two aspects of smoking gestures and cigarettes. Pundhir et al.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations