Abstract
Video acquisition has become more convenient as science and technology have progressed, and the development of mobile Internet has resulted in a large amount of video data being generated every day. The question of how to analyze these videos automatically has become urgent. Among them, the study of sports movement recognition in video has important theoretical implications in sports research as well as practical application value. This paper proposes a PSO-NN-based sports action recognition model. Kernel principal component analysis is used to extract and analyze the characteristics of sports movements. The improved neural network is used to identify common human postures in sports, and the classification and block background estimation method is used to detect human targets. The feature extraction of targets is completed according to the edge features, and the feature extraction of targets is completed according to the edge features. Finally, the feature vectors are trained using a backpropagation neural network (BPNN), and the parameters of the BPNN are chosen using the PSO algorithm to create a classifier for sports action recognition. The results show that this model improves the accuracy of sports video recognition and is an effective method of sports action recognition when compared to the comparison model.
Highlights
With the development of network technology and computer intelligent monitoring technology, a large number of video data came into being [1]. e traditional manual analysis method can no longer meet the current demand for video analysis of specific targets, so intelligent video data processing has emerged as a major issue [2]. e field of vision and sensors is currently the primary research hotspot in motion recognition
Motion recognition based on sensors, on the other hand, primarily employs microsensors and processes data using machine learning methods [3]. e majority of sporting performances are achieved by watching sports videos, which is unquestionably time and labor-intensive. e target detection and deep learning (DL) [4] technologies offer new approaches to resolving this issue
With the advancement of video technology, sports video data are rapidly increasing, which is critical for detecting sports movements in sports videos
Summary
With the development of network technology and computer intelligent monitoring technology, a large number of video data came into being [1]. e traditional manual analysis method can no longer meet the current demand for video analysis of specific targets, so intelligent video data processing has emerged as a major issue [2]. e field of vision and sensors is currently the primary research hotspot in motion recognition. Human motion recognition is an important field of computer vision research, and its purpose is to analyze the ongoing human activities in the video [5]. With the progress of science and technology, especially the development of mobile Internet, video acquisition is more convenient, and a large number of video data are generated every day. How to analyze these videos automatically has become an urgent problem [6, 7]. Wireless Communications and Mobile Computing network (NN) model [10] can be directly applied to raw data to automatically extract feature values, eliminating the tedious process of manually extracting feature values. In order to obtain more ideal sports action recognition results, a sports action recognition model of particle swarm optimization neural network (PSO-NN) is proposed. is model can directly process the time series data, automatically extract feature values, and avoid the tedious process of manually extracting feature values
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
