Recognition Algorithm of Basketball Dynamic Movement Behavior Based on Multimedia Network Technology

In order to realize the recognition of basketball's dynamic sports behavior in the dynamic scene of competition and training based on computer vision technology, combined with video data analysis, and improve the standardization of basketball's dynamic sports behavior, this paper puts forward a recognition method of basketball's dynamic sports behavior in the dynamic scene of competition and training based on multimedia network, and adopts the method of optical flow acquisition to collect RGB image data during the conversion process of basketball's dynamic sports behavior in the dynamic scene of competition and training. The collected optical flow field of basketball dynamic motion in the dynamic scene of competition and training is extracted with normative features, and the video parameters of basketball dynamic motion in the dynamic scene of competition and training are analyzed with the method of multimedia network technology video supervised learning. Under the constraint of dynamic basketball vision and optical flow field changing parameters, the temporal and spatial features extracted from basketball dynamic motion sequence are extracted by two-degree-of-freedom dynamic parameter model parameter learning. Combining the techniques of feature extraction, feature fusion and feature classification, this paper analyzes the optical flow trajectory parameters in the process of basketball dynamic movement behavior transformation, and realizes the dynamic parameter identification of basketball dynamic movement behavior in the dynamic scene of competition and training by combining the scene attributes and the distribution of time and space points of interest. The simulation results show that this method can effectively recognize the static and dynamic characteristics of basketball dynamic movement behavior in the dynamic scene of competition and training, and improve the dimension of basketball dynamic movement behavior recognition, thus effectively realizing the accurate detection and recognition of basketball dynamic movement behavior under complex background and multi-person interaction.