Bridging the semantic gap in sports

Li, Baoxin; Errico, James H.; Pan, Hao; Sezan, M. Ibrahim

doi:10.1117/12.476261

Cited by 9 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early research focused on automatic shot boundary detection and key frame extraction, using low-level features such as motion and color. More advanced work focused on detection and classification of specific high-level events in particular domains, such as segmentation of stories in news broadcasts [14] or, more recently, detection of plays in sports video programs [15]. Other work includes analysis of documentaries [16] and home movies [17].…”

Section: Automatic Summarization Techniquesmentioning

confidence: 99%

Metadata-driven multimedia access

Beek¹,

Smith²,

Ebrahimi

et al. 2003

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

Section: Automatic Summarization Techniquesmentioning

confidence: 99%

Metadata-driven multimedia access

Beek¹,

Smith²,

Ebrahimi

et al. 2003

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

“…Thus, a replay scene can be taken as a significant indication of a highlight [9]. We use the replay detection to localize the highlights in this work.…”

Section: Highlight Detectionmentioning

confidence: 99%

Highlight ranking for sports video browsing

Tong

Liu

Zhang

et al. 2005

Proceedings of the 13th Annual ACM International Conference on Multimedia

View full text Add to dashboard Cite

Sports video has been extensively studied for its wide viewer-ship and tremendous commercial potentials. Many studies focused on highlight extraction for summarizing a lengthy video. In this paper, we present an advanced highlight analysis system for sports video browsing, in which highlight evaluation and ranking are concerned besides highlight detection. First, we use replay detection to efficiently localize the highlights. Then incorporating with domainspecific knowledge, we adopt several significant cues to evaluate the importance degree of the highlights with support vector regression. Finally, the highlights are ranked with descending sort according to their importance value. The ranking results can provide a hierarchical video browsing and customized content delivery scheme. Initial experimental results on soccer videos show an encouraging performance comparing with human subjective evaluation.

show abstract

“…It possesses very detailed information about the event, related players/team and approximate event time, and hence some researchers have attempted using web-casting text for sports video analysis. For example, Li et al [93,94,95,96] proposed a generic sports video indexing framework by play data detection, scoreboard OCR, web-casting text analysis and the synchronization of these three information; Xu et al [97] utilized the time-stamp in web-casting text to synchronize the text event and the event from visual/audio analysis. Examples of web-casting text can be found in [98, 99, 100, 101].…”

Section: Web-casting Textmentioning

confidence: 99%

“…Some previous researches have attempted similar work, e.g., Babaguchi et al [51] matched the shot sequence located during the time interval given by text (CC) analysis with example sequence to identify the sports highlight as well as the highlight's boundary. Li et al [95,93] performed scoreboard OCR to generate synchronization points for detected video events and used Dynamic Programming (DP) to find best matching between these synchronization points with successive time-stamps in the web-casting text to obtain event semantics. Their methods are limited as they required the time tag to be accurate and well synchronized with the video.…”

Section: Alignment Of Text Event and Videomentioning

confidence: 99%

Content-based sports video analysis and composition

Wang¹

View full text Add to dashboard Cite

This thesis proposes solutions for content-based sports video analysis, including multimodal feature extraction, middle-level representation and semantic event detection. In addition, solutions for sports video composition and personalization are also examined. The first part of the thesis describes our methodology to detect semantic events and event boundaries from both broadcast sports video and non-broadcast sports video. Specifically, to process broadcast sports video, our approach not only uses visual/audio features, we also analyze the web-casting text information associated with the video and synchronize it with the visual/audio features to detect event, locate event boundaries and identify involved players/teams; To process non-broadcast sports video, we select the raw unedited main-camera soccer video as the input and use visual, audio and motion features extraction with multi-level modeling to detect event and event boundaries. Our proposed techniques are evaluated using large sports video data set and achieved satisfactory user acceptance score. The second part of the thesis introduces three novel applications based on our proposed sports video analysis techniques. The first application is a live sports highlight generation system which can automatically detect multiple events from a live sports game and extract a suitable video segment for each event to provide live and personalized game viewership via various mediums. The second application attempts to automatically generate broadcast soccer video composition from multiple raw video captures by detecting events from unedited soccer video and mimicking human director's practice to control the replay insertion and camera view switching operations. The third application is a personalized music sports video generation system to automatically select and align desired sports video scenes with music clips to generate music video clips. The three proposed systems are tested using objective evaluations and subjective user studies. i ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library Many people have contributed their ideas, time, and energy in my pursuit of this PhD research. I wish it was possible to thank them all. First thanks must go to my supervisor, Dr. Chng Eng Siong, and co-supervisor, Dr. Xu Chang Sheng, who have been truly inspirational throughout my candidature. I would like to thank them for all their guidance, rewarding discussions, cooperation, encouragements, and lasting support to my study and my life. I have been blessed to find in them all the good qualities of a supervisor. I am indebted to the School of Computer Engineering, Nanyang Technology University (NTU), for offering me the PhD scholarship. I am also thankful to the Institute for Infocomm Research (I 2 R) and the Centre for Multimedia and Network Technology, NTU for providing me an excellent working environment with all the much needed facilities and services, as well as financial supports including travel allowances....

show abstract

Bridging the semantic gap in sports

Cited by 9 publications

References 14 publications

Metadata-driven multimedia access

Metadata-driven multimedia access

Highlight ranking for sports video browsing

Content-based sports video analysis and composition

Contact Info

Product

Resources

About