Abstract
The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.
Highlights
In the field of sports and athletics, the standard of the basketball player’s action is the key to determine the athlete’s performance. e traditional scoring method relies on the human eye to score, which may cause a large error or injustice
Deep convolutional neural network is a special type of neural network
Is kind of convolutional neural network is mainly composed of input layer, convolution layer, activation function, pooling layer, fully connected layer, and output layer
Summary
In the field of sports and athletics, the standard of the basketball player’s action is the key to determine the athlete’s performance. e traditional scoring method relies on the human eye to score, which may cause a large error or injustice. Many scholars at home and abroad have conducted related researches on three aspects: feature collection, deep convolutional neural networks, and human action recognition. From the perspective of deep learning, this article extracts and recognizes the dynamic human movements of basketball players, and uses Bi-LSTM neural network and TensorFlow to identify and simulate human basketball training, calculates the accuracy of simulation experiment results, and collects basketball players' movement data. E innovation of this article is to apply the convolutional neural network in deep learning to the action analysis of basketball players, which can improve the efficiency of athletes’ training and improve their physical fitness and competitive ability From the perspective of deep learning, this article extracts and recognizes the dynamic human movements of basketball players, and uses Bi-LSTM neural network and TensorFlow to identify and simulate human basketball training, calculates the accuracy of simulation experiment results, and collects basketball players' movement data. e innovation of this article is to apply the convolutional neural network in deep learning to the action analysis of basketball players, which can improve the efficiency of athletes’ training and improve their physical fitness and competitive ability
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