Gesture Recognition of Human-Computer Interaction Image Based on Mathematical Statistics
The application prospect of gesture recognition in the field of human-computer interaction has brought infinite convenience to human beings in many fields.Based on mathematical statistics features,a method of gesture recognition for human-computer interaction images is designed,which realizes gesture information ac-quisition based on mathematical statistics features and human-computer interaction based on recognition results.A series of preprocessing such as image gray-scale processing,binarization processing,smoothing processing,edge detection and contour extraction are implemented for human-computer interactive images.Seven Hu mo-ments with scale invariance,rotation invariance,and translation invariance are extracted from binary human-computer interaction images using OpenCV.The first four moments describe the physical quantities of the ges-ture's image ellipse,principal axis direction angle,area,and rotation radius,while the last three moments de-scribe image symmetry,center of gravity,and center distance.The backbone network of YOLO-V2 network is improved based on Darknet-19,so that the number of anchor boxes that can be predicted by the model reaches 16×16×N pieces.The gesture recognition model is designed based on the improved YOLO-V2 network.The input of the model is the human-computer interaction graph,the extracted gesture contour and Hu moment,and the interactive image gesture recognition is realized.The test results show that the design method has a high ac-curacy of gesture recognition both indoors and outdoors,and the human-computer interaction can be realized through the gesture recognition results of this method.
Hu momentgrayscale imagesOpenCVhuman machine interaction imagesimprove YOLO-V2network gesture recognition
万方数据
维普
