Abstract
Passing is a relatively basic technique in volleyball. In volleyball passing teaching, training the correct passing technique plays a very important role. The correct pass can not only accurately grasp the direction of the ball point and the drop point but also effectively connect the defense and the offense. In order to improve the efficiency and quality of volleyball passing training, improve the precise extraction of sport targets, reduce redundant feature information, and improve the generalization performance and nonlinear fitting capabilities of the algorithm, this paper studies volleyball based on the nested convolutional neural network model and passing training wrong movement detection method. The structure of the convolutional neural network is improved by nesting mlpconv layers, and the Gaussian mixture model is used to effectively and accurately extract the foreground objects in the video. The nested multilayer mlpconv layer automatically learns the deep-level features of the foreground target, and the generated feature map is vectorized and input to the Softmax classifier connected to the fully connected layer for passing wrong behavior detection in volleyball training. Based on the detection of nearly 1,000 athletes’ action datasets, the simulation experiment results show that the algorithm reduces the acquisition of redundant information and shortens the calculation time and learning time of the algorithm, and the improved convolutional neural network has generalization performance and nonlinearity. The fitting ability has been improved, and the detection of abnormal volleyball passing behaviors has achieved a higher accuracy rate.
Highlights
After the Gaussian mixture model of each pixel is generated, the Gaussian distribution is arranged in descending order according to the value of ψ/μ, and the first B Gaussian distribution is obtained as the background model [20]. e formula is as follows: b
When using the method in this paper to detect the wrong action of volleyball passing training, the error can be controlled within a reasonable area
Since the traditional methods cannot accurately obtain the characteristics of the wrong movements in volleyball passing training, resulting in the decrease of detection accuracy, this paper proposes a method for detecting the wrong movements in volleyball passing training based on a convolutional neural network. e convolutional neural network structure is improved by the nested mlpconv layer. e mixed Gaussian model is used to extract the passing target from the volleyball training video sequence effectively and accurately
Summary
With the further development of research in related fields, convolutional neural networks have been widely used in the process of human action recognition [11] and target detection [12] It can realize the further processing of the action samples, which leads to the poor applicability of applying this method to the detection of wrong actions in physical education training. Erefore, in order to make up for the abovementioned deficiencies in the process of volleyball passing training wrong action detection, this paper proposes an improved convolutional neural network crowd abnormal behavior recognition method, which improves the convolutional neural network structure by nesting mlpconv layers and uses a mixture of Gaussian models to effectively and accurately extract foreground targets in volleyball passing videos.
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