Method for detecting goal events in soccer video based on hidden markov model

Abstract

The invention discloses a method for detecting goal events in a soccer video based on a hidden markov model. By the method, the problems of complicated event detection system model and low detection rate in the prior art are solved. The method comprises the following steps of: firstly, performing physical shot segmenting and semantic shot labeling on a training video and a test video, and respectively forming a training data set and a test data set respectively by using acquired semantic shot sequences; secondly, calculating an initial parameter of the hidden markov model according to the training data set; thirdly, training an initial model by adopting a Baum-Welch algorithm and the training data set and establishing the hidden markov model for the goal events; fourthly, calculating a probability of the model for generating training data by adopting a forward algorithm and acquiring a judgment threshold value; and finally, calculating a probability of the model for generating test data and detecting the goal event in the test video according to the judgment threshold value. By the method, detection for semantic goal events can be realized accurately; and the method is applied to the field of semantic analysis, such as detection for wonderful events in the soccer video and the like.

Description

technical field [0001] The invention belongs to the field of video information retrieval, relates to sports video semantic analysis, and can be used in football video goal event detection to detect goal events quickly and accurately. Background technique [0002] Sports video has attracted extensive attention from researchers and all walks of life because of its huge audience and huge commercial value. Automatic detection of exciting events in sports videos has always been a hot spot in the field of video semantic analysis. The difficulty lies in the need to solve the semantic gap between low-level features and high-level semantics. Scholars at home and abroad have conducted extensive research on this and achieved a lot of research. results. [0003] The current methods based on machine learning mainly include: [0004] (1) Ding Y, Fan G L. Sports Video Mining via Multichannel Segmental Hidden Markov Models [J] IEEE Trans. on Multimedia, 2009, 11(7): 1301-1309. Based on t...

AI Technical Summary

Problems solved by technology

However, the process of building a model with this method is time-consuming, and hidden state variables are not used, so the potential laws of semantic events cannot be mined more effectively, which limits the improvement of detection performance.

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