Application Study of Area-Based and YOLOv4 Smoking Behavior Detection
Chenbing Bai, You Zhou, Qingkai Guo · 2022 4th International Conference on Natural Language Processing (ICNLP) · 2022
Due to the cigarette targets are not obvious, quality of monitoring picture is not clear and other factors lead to slow detection and feature extraction difficulties for the actual monitoring process. This paper proposes a target detection scheme of cigarettes with using the fusion of Yolov4 target detection algorithm and feature extraction of the human body region algorithm based on HOG. This paper uses the Yolov4 target detection algorithm as the main framework, intending to shorten the time of cigarette detection and ensure the real-time monitoring process. To solve the problem of difficult feature extraction for cigarette targets detection and effectively reduce the CPU usage, it makes a preliminary check of the human area for the presence of smoke while adding the hog based human area extraction algorithm to the algorithm before the cigarette targets detection. Experiments show that the method can effectively solve the problem of cigarette targets detection in public places. Compared with the original faster region convolution algorithm, the detecting time of a single image slightly increases but still meets the requirements of practical engineering applications. On the other hand, the rate of detecting incorrectly is reduced by about 2% and the detection accuracy is significantly improved.