Abstract
In the field of basketball, the formulation of the existing training plan mainly relies on the coaches’ artificial observation and personal experience, which is inevitably subjective. The application of body domain network technology in athletes’ training and recognition of athletes’ postures can help coaches to assist decision-making and greatly improve athletes’ competitive ability. The human movements reflected in basketball are more complex which need deep understanding. The accuracy of basketball players’ shooting movements recognition plays a positive and important role in basketball games and training practice. Based on the prior knowledge of the convolutional neural network study, environment light conditions change the dynamic characteristics of basketball image analysis, capture images of the basketball goal algorithm of minimum circumscribed rectangle of the object, and based on the convolutional neural network, introduce two types of prior knowledge, one kind is based on the feature matching method that defined a priori knowledge, while another kind is based on training the convolution neural network model. The test results of the network model are taken as the prior knowledge, and then, a convolutional neural network dynamic target recognition model is constructed based on the prior knowledge. The construction process of the model is organized as the basketball target image is collected under any illumination conditions, the convolutional neural network model is trained with the convolutional neural network as the input data, and the standard illumination conditions are determined according to the test results of the network model. Then, put it into the trained network model to test and get the recognition results of basketball players’ shooting movements. The research is validated with performing experiments and the results revealed the success of the study.
Highlights
Basketball is a collective sport that put the ball into the opponent’s basket to score and prevents the opponent from getting the ball and scoring under certain rules
Compared with other ball games, basketball has a variety of techniques [1,2,3], diverse tactics [4], and strong skills of players [5,6,7]
In order to improve the rapidness and accuracy of object recognition under the condition of illumination change, this study proposes a convolutional neural network based on prior knowledge to recognize basketball player’s shooting image
Summary
Basketball is a collective sport that put the ball into the opponent’s basket to score and prevents the opponent from getting the ball and scoring under certain rules. Scientific Programming the testing of athletes is performed manually, which requires a lot of time for the coaches, the process is complicated, and the accuracy is poor; second, the testing methods have limitations, and it is difficult to directly measure some important sports parameters such as acceleration and angular velocity Information such as muscle tension, sprinting ability, and body balance cannot be measured during exercise. Iosifidis et al [24] used multiple cameras to perform multiangle detection of human action poses and used neural network algorithms to image and video data that are trained and classified This method can identify people’s daily actions more accurately, it is difficult to realize real-time monitoring due to the large amount of data contained. (ii) Based on the analysis of the traditional recursive model, this study uses a special form of the recursive neural network LSTM to predict the law of multimedia communication in graphic design, carries out a comparative experiment, and the experimental results prove the superiority of the proposed algorithm
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