Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

INTRODUCTION: Sports video is an essential information resource. Classifying sports videos with high accuracy can effectively improve users' browsing and query effect. This project intends to study a motion video classification algorithm based on deep-learning particle filters to solve the problems of solid subjectivity and low accuracy of existing motion video classification algorithms. A critical box extraction method based on similarity is proposed. The moving video classification algorithm is studied based on a deep learning coding model. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can significantly improve the classification performance of motion video.