Comparative Analysis Of Video Actions Based On Human Pose Estimation

In recent years,with the continuous improvement of people's living standards,the development of sports has been very rapid,and at the same time,the disadvantages of traditional sports learning methods have gradually emerged.In the traditional sports mode,you can basically only rely on the coach to give you guidance to know whether each action is standardized,but this one-to-one coaching method has led to the tension and scarcity of coaching resources.This largely hinders the further development of the sport and the progress of each athlete in the sport.At the same time,with the development of science and technology,computer vision has been continuously integrated into all parts of human society,which can help people live more conveniently.Based on this,this paper proposes a new research method for video comparison analysis based on human pose estimation and clustering.The method mainly includes two major directions,video human pose estimation and video key frame comparison.For these two directions,this paper proposes three innovations:First,a lightweight human pose estimation model is proposed.Based on the HRNet human pose estimation model,this model uses depthwise separable convolution to complete the improvement of the network basic block,and tailors the network structure to further complete the lightweight operation of the model.These improvements enable the Small-HRNet model proposed in this paper to greatly reduce the scale and parameter amount of the model while maintaining the detection accuracy,and at the same time use more efficient DARK technology for the encoding and decoding parts of the original model.The accuracy of the model has been further improved.Finally,experiments are carried out on the lightweight human pose estimation model proposed in this paper on two public datasets,COCO and MPII.The second is to propose a method to obtain key frames of motion video based on skeleton information and clustering.This method first uses the lightweight human pose estimation model proposed in this paper to estimate the human pose of a single person in the video,and obtains the single-person skeleton joint point information of the entire video;The single human skeleton joint point information of the frame is subjected to data preprocessing operation to obtain the rotation angle data,and a series of rotation angle information is used as the motion feature;finally,the standard frame of each action is used as the clustering center,and each video frame is used as the The samples are clustered,and the sample frame closest to the cluster center is selected as the key frame to be acquired.The key frame acquisition method has been tested in the martial arts dataset constructed in this paper.The third is to propose a basic framework for comparative analysis of martial arts movements,which is mainly composed of three parts: video human pose estimation,key frame acquisition and key frame comparison.In the key frame comparison,this paper refers to the martial arts comparative analysis specification given by professional martial arts coaches in detail,and designs local features for the detailed comparison,such as arm straightening feature,leg straightening feature,shoulder and horizontal angle features,etc.For most sports,it is very convenient and feasible to complete the entire motion video comparison and analysis process by appropriately changing a few feature parameters in the key frame comparison.