Event detection in field sports video using audio-visual features and a support vector Machine

Sadlier, David A.; O’Connor, Noel E.

doi:10.1109/tcsvt.2005.854237

Cited by 204 publications

(135 citation statements)

References 38 publications

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“…Shot view classification contains detection of various views like Long view, close up view, medium view and out of field view. There are various techniques proposed [1] [4], some of the techniques use dominant color of frames for view classification. For close up view dominant color can be skin color of the player, for long view we can say that dominant color will be green as the background of the soccer field is green.…”

Section: Summarizing Sports Videosmentioning

confidence: 99%

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

Khan¹,

Pawar²

2015

ijacsa

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Abstract-In today's world, the rapid development of digital video and editing technology has led to fast growing of video data, creating the need for effective and advanced techniques for analysis and video retrieval, as multimedia repositories have made browsing, delivery of contents (video) and video retrieval very slow. Hence, video summarization proposes various ways for faster browsing among a large amount of data and also for content indexing. Many people spend their free time to watch or play different sports like soccer, cricket, etc. but it is not possible to watch each and every game due to the longer timing of the game. In such cases, the users may just want to view the summary of the video that is just an abstract of the original video, instead of watching the whole video that provides more information about the occurrence of various incidents in the video. It is preferable to watch just highlights of the game or just review/trailer of a movie. Apparently, summarizing a video is an important process. In this paper, video summarization approaches are discussed, that can generate static or dynamic summaries. We present different techniques for each mode in literature. We have discussed some features used for generating video summaries. As soccer is the world's most famous game played and watched, it is taken as a case study. Research done in this domain is discussed. We conclude that there is a broad perspective for further research in this field.

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Section: Summarizing Sports Videosmentioning

confidence: 99%

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

Khan¹,

Pawar²

2015

ijacsa

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“…It has been shown in [4] that about 97 % of interesting moments during a game are followed by a close-up shot presenting a player who scored or who caused some interesting action. In addition, features like end of a pitch, audio activity, or crowd shot detection have been shown to be very useful in event detection [4]. The system present in [4] was proven to work with different sports such as soccer, rugby, field hockey, hurling, and Gaelic football.…”

Section: Sport Event Detection Approachesmentioning

confidence: 99%

“…In addition, features like end of a pitch, audio activity, or crowd shot detection have been shown to be very useful in event detection [4]. The system present in [4] was proven to work with different sports such as soccer, rugby, field hockey, hurling, and Gaelic football. A Support Vector Machine (SVM) was used as a event classifier.…”

Section: Sport Event Detection Approachesmentioning

confidence: 99%

“…It is worth noting that the three approaches described above [4][5][6] are capable of extracting not only goals but also other exciting moments like penalties and near misses. In [7], very simple features like pixel/histogram change ratio between two consecutive frames, grass ratios, and background mean and variation in addition to time and frequency domain audio features were used to detect events in soccer games.…”

Section: Sport Event Detection Approachesmentioning

confidence: 99%

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Real-Time Event Detection in Field Sport Videos

Kapela¹,

McGuinness

Świetlicka³

et al. 2014

Computer Vision in Sports

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This chapter describes a real-time system for event detection in sports broadcasts. The approach presented is applicable to a wide range of field sports. Using two independent event detection approaches that work simultaneously, the system is capable of accurately detecting scores, near misses, and other exciting parts of a game that do not result in a score. The results obtained across a diverse dataset of different field sports are promising, demonstrating over 90 % accuracy for a feature-based event detector and 100 % accuracy for a scoreboard-based detector detecting only scores. Event Detection in SportsSport has always been one of the most popular of television broadcasts [1,2]. Sports broadcast viewing figures are consistently high, particularly for events like the Olympics and national or regional finals of nationally popular games. Given such wide appeal and popularity, there has been significant interest in algorithms for automatic event detection in sports broadcasts [3]. This is motivated by potential applications such as automatic highlight generation for summarization for emerging second screen applications, indexing for search, and retrieval in large archives, mobile content delivery either offline or as an added value in-stadium user experience.

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“…Thus, multimedia techniques that result in elaborate visualisation or explorative interaction is less of a value to mobile platforms but summarising, structuring, and selective pushing types of techniques seem more promising for mobile applications. For example, the sports summarisation technique we have developed [24] analyses any field-sports video content and identifies those segments that contain high probability of important events happening in the game. Stitching up those identified segments can result in a 3-minute video summary of important events from a 90-minute football match.…”

Section: Interaction Platform Issuementioning

confidence: 99%

Issues in Designing Novel Applications for Emerging Multimedia Technologies

Lee

2010

HCI in Work and Learning, Life and Leisure

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Abstract. Emerging computational multimedia tools and techniques promise powerful ways to organise, search and browse our ever-increasing multimedia contents by automating annotation and indexing, augmenting meta-data, understanding media contents, linking related pieces of information amongst them, and providing intriguing visualisation and exploration front-ends. Identifying real-world scenarios and designing interactive applications that leverage these developing multimedia technology is certainly an important research topic in itself but poses a number of challenges: the currently practiced methodologies and tools in the field of Human-Computer Interaction and Interaction Design seem to work better when the target users and usage requirements have been clearly identified and understood in advance whereas much of what emerging multimedia technology could offer is expected to create completely new user activities and usage that we are not aware of; immature multimedia tools currently being researched are not good enough to be the core engines of real-world applications today, making realistic user studies through deployment difficult; our future interaction platforms will be more than just desktop PC, Web, or mobile devices but many other forms of tangible, embedded, physical appliances which we expect the currently developing multimedia technology would be coupled with. In this paper, these challenges and the insights into how we could get over them are explored based on the author's decade-long experience in designing novel interactive applications for multimedia technology.

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Event detection in field sports video using audio-visual features and a support vector Machine

Cited by 204 publications

References 38 publications

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

Real-Time Event Detection in Field Sport Videos

Issues in Designing Novel Applications for Emerging Multimedia Technologies

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