Spatial-temporal Correlation based Event and Tactics Semantics Analyses for Sports Videos

• Main Authors: Min-Chun Hu, 胡敏君
• Other Authors: 吳家麟
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/67879267229328864355

博士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 99 === With the popularization of digital camera and recorder, the amount of multimedia video content increases rapidly. The viewer will take a lot of time to exhaustively search for a specific video clip without proper management and annotation of all his/her video content. Consequently, videos will be kept in the storage device permanently and finally lose their value. Recently, techniques of automatic video semantic analysis have been proposed for different types of videos. Among all kinds of videos, sports videos are rich in highlights which can be extracted for many applications. In this dissertation, we proposed a comprehensive framework of sports video analysis purposing to bridge the gap between low-level features and high-level semantics in a video. Combining domain knowledge and spatial-temporal correlation of media features, we extract important semantics of events and tactics from sports videos. There is a great diversity in the audiences of sports video, and we roughly categorize them into three groups, i.e., the general audiences who desire exciting highlights, the beginner who crave strategies or skills of how to play, and the professionals who dig into tactics of the opponents. The proposed framework extracts various kinds of semantics to meet their requirements correspondingly. Three main modules, including media feature extraction, explicit event/tactics detection based on high-level media features, and implicit semantic concept retrieval based on trajectory similarity, are studied in this dissertation. For the media feature extraction module, we develop several kinds of robust object detectors to generate mid/high-level media features for concept detection/retrieval. Combining representative high-level media features and the given domain knowledge, the concept detection/retrieval modules are able to acquire spatial-temporal related semantics that hardly be discovered by low-level media features. Moreover, we explore the possibility of extracting implicit events/tactics which are usually ignored by conventional event detection models. In sports videos, these implicit concepts are related to the movements of players and ball. We design an interactive interface for the user to input query trajectories, and the technique of trajectory similarity comparison is utilized to recommend the video clips having the best matched object trajectories. We realized the proposed framework on three types of sports videos, including tennis video, billiards video, and basketball video, to demonstrate the feasibility of the proposed sports video analysis framework.