Event detection and semantic annotation method for snooker game videos

Abstract

The invention discloses an event detection and semantic annotation method for snooker game videos. The method includes the steps of detecting a full-tabletop lens; extracting score strip information; conducting audio classification through a hidden Markov model; detecting a playback lens; achieving event detection and semantic annotation for the snooker game videos through integration of multi-mode information and combination of snooker domain knowledge. According to the method, the multi-mode information in the videos is integrated, event detection and semantic annotation of the content of the snooker game videos are achieved, and a solid foundation is laid for establishing video indexes based on semantics.

Description

technical field [0001] The invention belongs to the field of content-based video retrieval, and in particular relates to a snooker game video event detection and semantic labeling method. Background technique [0002] Semantic content-based video analysis has always been a hot topic in the field of multimedia research. Sports video, as an important video resource, has a huge audience base and commercial value, and has received extensive attention from relevant scientific research institutions and researchers. With the rapid increase in the number of sports videos and the rapid popularization of mobile computing devices in the triple play environment in recent years, people are no longer satisfied with the current status of sports video viewing and retrieval, but have more opportunities to quickly locate the video content they are interested in. The key to solving this problem is to automatically detect and label semantic events in videos. [0003] Billiards is one of the t...

AI Technical Summary

Problems solved by technology

Although there have been a small amount of research results on snooker video analysis, these achievements still have a certain distance to build an event detection and labeling system based on semantic content, which is reflected in the following aspects: 1. The analysis work of Nock game video all focuses on the automatic generation of video summaries and the three-dimensional reconstruction of the game process, and there is no related research on event detection and labeling, and the existing research results are far from enough to realize content-based video Retrieval; 2. Existing research on video summaries of snooker games often simply record the entire game process in a journal style, without distinguishing the importance of various events, and also ignoring many important semantic events , although these summaries are of great help to professional players and coaches, most of them are not of interest to ordinary audiences, who are only interested in the exciting or key events in the game; 3. The existing The analysis of Nock game video only relies on a single visual information for analysis, while ignoring the text and audio information that contains a lot of semantic information, which is a huge flaw in the existing research work

Images