Fast TLAM: High-precision Fine Grain Smoking Behavior Detection Network

Zhang, Yang; Yao, Dengfeng

doi:10.1109/icicsp50920.2020.9232100

Cited by 1 publication

(4 citation statements)

References 19 publications

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“…Compared to [28], which used only CNN training network structure to detect cigarettes, Yang et al [29] used a k-means algorithm to cluster the candidate frames, which improved the accuracy of detection boxes. Similar to [29], Zhao et al [30] also used k-means clustering for candidate regions, the difference is that they proposed to add a small object detection layer, which had better detection results for small objects such as cigarettes. This also inspires us to add a small object detection layer in the proposed method to improve the detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…This method works well in simple scenes, but there are still many false detections in complex backgrounds. Compared to [28], which used only CNN training network structure to detect cigarettes, Yang et al [29] used a k-means algorithm to cluster the candidate frames, which improved the accuracy of detection boxes. Similar to [29], Zhao et al [30] also used k-means clustering for candidate regions, the difference is that they proposed to add a small object detection layer, which had better detection results for small objects such as cigarettes.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to [28], which used only CNN training network structure to detect cigarettes, Yang et al. [29] used a k‐means algorithm to cluster the candidate frames, which improved the accuracy of detection boxes. Similar to [29], Zhao et al.…”

Section: Related Workmentioning

confidence: 99%

“…[29] used a k‐means algorithm to cluster the candidate frames, which improved the accuracy of detection boxes. Similar to [29], Zhao et al. [30] also used k‐means clustering for candidate regions, the difference is that they proposed to add a small object detection layer, which had better detection results for small objects such as cigarettes.…”

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

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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.

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