Attitude-assisted correction framework for cheerleading based on visual tracking technology

Abstract

Attitude-assisted correction framework for general cheerleading based on visual tracking technology is presented in this paper. In the proposed model, the contributions are focused on the three aspects. (1) A tracking method based on basic diverse examples of learning is proposed. Drift is mitigated by relaxing the selection area of the naive positive sample. (2) The distinction analysis is one of basic pattern recognition research important branches, its essential target is through to the known data sample training and the study, the mentioned ideas are used to construct the novel models. (3) The comprehensive evaluation model based on the data mining framework is discussed, the core fuzzy clustering model is presented. The simulation is also well presented to show the performance of the idea. The future research plan is summarized in the final for references.