Human Abnormal Behavior Recognition Method Based on YOLO-CARAFE
In intelligent monitoring,there are many factors such as complex environment,multiple monitoring targets,poor picture quality,and different personnel sizes,which bring many challenges to human abnormal behavior recognition.In order to improve the accuracy and efficiency of human abnormal behavior recognition in video,we propose YOLO-CARAFE for human abnormal behavior recognition.In this method,CARAFE,a lightweight up-sampling operator,is first used to replace the nearest neighbor interpolation up-sampling operator.CARAFE not only works with adjacent pixels,but also performs weighted fusion of adjacent pixels,which can aggregate context information in the large sensing field,thereby improving the feature extraction and fusion capability of neural networks in recognizing human abnormal behaviors in complex scenes.Secondly,using the difficulty sample learning strategy of Focal-EIOU loss function,the model pays more attention to the target objects that are difficult to classify,effectively reduces the difference between the prediction frame and the real frame,improves the accuracy of human abnormal behavior identification,and effectively solves the characteristics of small amount of abnormal behavior sample data.Experiments on self-built data sets show that YOLO-CARAFE has a good recognition effect on human abnormal behavior recognition.When R is unchanged,mAP@0.5 and P of the proposed YOLO-CARAFE algorithm are96.9% and97.6% respectively,increasing by1.9 percentage points and7.4 percentage points.It can meet the accuracy and real-time requirements of abnormal behavior identification in surveillance video.
万方数据
维普
