Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning

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.

Recognition of Human Body Feature Changes in Sports Health Based on Deep Learning

“…And experiments have shown that this method can increase the accuracy of TOP-1 by 0.6% In order to solve the problem of gradient disappearance caused by the deepening of the network layer, the network designed two branches and added two softmax classifiers at the end of the branches to assist in adjusting the network parameters. Some researchers have proposed to add attenuation coefficients to the two classifiers, but in the end, it is proved through experiments that the improvement is not very meaningful [ 29 , 30 ]. When we actually test, these two extra softmax will be removed, only based on the output result of the main softmax.…”

Sports Deep Learning Method Based on Cognitive Human Behavior Recognition

“…Wang et al used the optical flow feature of a video sequence to realize template matching, but the optical flow is easily affected by noise, so the effect of this method is limited [12]. Liu et al used the word bag model to realize the task of recognizing human behavior, and both achieved the expected recognition results.…”

“…Softmax function Computational Intelligence and Neuroscience maps the output of each dimension to the probability value between [0, 1], ensures that the sum of the output of all dimensions is 1, and then calculates its CE loss. Combine equations ( 9) and ( 11) to obtain the Softmax loss function, as shown in equation (12).…”

The Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

“…A quantitative comparison is made between the suggested method and cutting-edge techniques to verify its validity. The experimental outcomes are depicted in figure 3; the contrasted approaches include LRCN [34], ALSTM [35], VideoLSTM [36], and CHAM [37]. When compared to previous approaches, the ARBIGNet suggested in this study can reach 90.5% mAP and 96.5% accuracy, which may be enhanced to varied degrees, demonstrating its superiority.…”