Vision-based Realtime Gesture Recognition Technology And Its Application

With the development of technology,a natural human-computer interaction is urgent for the users.The natural human-computer interaction is user friendly.As an important technology of human-computer interaction,the gesture recognition has become a research hotspot in recent years.The gesture recognition can be divided into two categories: vision-based gesture recognition and inertial sensor-based gesture recognition.Compared to inertial sensor-based gesture recognition,vision-based gesture recognition is more important because of its intuition,convenience,and freedom.In this thesis,we carefully study the key techniques of vision-based gesture recognition,including gesture segmentation,gesture extraction,recognition of static gesture,and recognition of dynamic gesture.At last,we apply our vision-based gesture recognition technology in a human-computer interaction system to test its performance.This system can recognize three gestures: “scissor”,“rock”,“paper”,and let the user play a “scissor-rock-paper” game.Using the skin color information and the motion information,the gesture can be segmented accurately.The skin color information is obtained in real time by using the YCb Cr Gaussian skin color model;the background subtraction method is used to extract the motion region;the skin color region and the motion region are fused to obtain the skin color motion region;the morphological method is used to denoise the skin color region.The search algorithm extracts the gesture contour and denoises it through the connected region analysis to obtain the final gesture.For static gesture recognition,we extract the HOG features of the training samples,train the SVM model,and identify the samples according to the trained model.The HOG feature of the training sample is analyzed by the experiment.The experimental results show that this method can identify the different directions of the same gesture and achieves the average recognition accuracy at 93.08%.Considering the scale variability of HOG feature,this thesis adopts the volume semantic local binary patterns(VSLBP)algorithm to extract features,and uses SVM to design a classifier for real-time hand gesture recognition.Based on the three models of "scissors","rock" and "paper",the LBP algorithm is used to extract the feature to train the SVM model.The tested samples are identified according to the trained model.The experimental results show that the average recognition accuracy is 94.42%.Finally,this algorithm is applied in a human-computer interaction to realize the recognition three gestures: "scissors","rock" and "paper".So,this human-computer interaction can let the user play a "scissors-stone-cloth" game,as show our method can recognize the gestures in real time and can be used in practical applications.