摘要
Abstract
In order to reduce the incidence of laboratory accidents,a laboratory unsafe behavior detection system based on multi-source feature fusion is designed.The basic framework of the system is constructed,and high-definition camera is used to obtain laboratory monitoring video images in the image acquisition unit.After grayscale,denoising,and smooth filtering processing in the image processing unit,the video target area image is obtained by means of the frame difference method in the target recognition unit.Then,the video target area image is processed in the feature extraction unit,and HOG features and human behavior center of gravity features are extracted.By fusing the two,a one-dimensional feature vector is obtained,which is input into the behavior detection unit.The unsafe behavior is detected by means of the SVM classifier.The experimental results show that the system can effectively detect unsafe behaviors in the laboratory,with an average F1 index and recall rate of 98.94%and 99.15%,respectively.The number of error detections is relatively small.关键词
多源特征融合/实验室/不安全行为检测/多源特征提取/HOG特征/SVM分类器Key words
multi-source feature fusion/laboratory/unsafe behavior detection/multi-source feature exaction/HOG features/SVM classifier分类
电子信息工程
