Sports Video Key Pose Data Incremental Mining Algorithm Based on Deep Learning

Sports Video Key Pose Data Incremental Mining Algorithm Based on Deep Learning

In order to solve the problems of low classification performance, low statistical similarity and low mining accuracy of traditional data mining algorithms, an incremental mining algorithm for sports video key pose data based on depth learning is proposed. First, the training tag of depth learning is made by using analog signal matrix, and the implementation prospect of sports video key pose frame is extracted with Caffe (Convolutional Architecture for Fast Feature Embedding) open source framework. The interference region in key pose frame is removed by clustering algorithm, and the key pose region of sports video is obtained. Secondly, the SOFM (Self-Organizing Feature Map) network is used to cluster the data of the key pose area of sports video, and the incremental mining model of the key pose data of sports video is established, and the data acquisition operation is carried out. The incremental mining parameters of key pose data of sports video are obtained by using the combined paradigm, finally, the mining parameters are input into the mining model, and the incremental mining of data is realized by using bwmorph method. The experimental results show that the key pose classification performance of the algorithm is much higher than that of the traditional sports video key pose data mining algorithm, the statistical similarity is high, and the method has higher mining accuracy and is more suitable for the mining of the key gesture data of the sports video.