Event Detection and Summarization in Soccer Videos Using Bayesian Network and Copula

Tavassolipour, Mostafa; Karimian, Mahmood; Kasaei, Shohreh

doi:10.1109/tcsvt.2013.2243640

Cited by 93 publications

(62 citation statements)

References 27 publications

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“…The proposed system in [24] is capable of detecting seven events in soccer games such as goal, foul, non-highlights, card, goal attempt, corner, offside, etc. It uses Chow-Liu Tree for structure estimation of Bayesian Network.…”

Section: Literature Review (Soccer Game As Case Study)mentioning

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: Literature Review (Soccer Game As Case Study)mentioning

confidence: 99%

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

Khan¹,

Pawar²

2015

ijacsa

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“…Additionally, these systems are usually proposed for specific situations (e.g., for a given type of sport); otherwise, they will not be able to analyze the video efficiently. Some of the recent event detection systems for sport videos such as [19,20,21,22,23] fall into this category. Almost all of these methods partition the video to high-level segments called play-break or highlights.…”

Section: Introductionmentioning

confidence: 99%

A framework for dynamic restructuring of semantic video analysis systems based on learning attention control

Sigari

Soltanian‐Zadeh

Pourreza

2016

Image and Vision Computing

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“…However, mainly because of the use of the Hough transform the implementation is very time consuming and 65 inapplicable in real-time systems. A very similar approach is presented in [16].…”

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confidence: 99%

“…However, again no time performance is given in the paper and there is a big drop in accuracy when the SVM is trained on the samples that do 75 not belong to the same game. It is worth noting that the three approaches described above [13,16,17] are capable of extracting not only goals but also other exciting moments like penalties or close misses. In [18], very simple features like pixel/histogram change ratio between two consecutive frames, grass ratio and background mean and variation in addition to time and frequency domain 80 audio features were used in order to detect events in soccer games.…”

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confidence: 99%

“…As a result, scene recognition, by which we mean recognizing what kind of camera shot is being used by a sports director at any given moment, is useful input for event detection. Although previous works [13,16,17,24] Since we described our investigation of the choice of the scene/shot detection 170 and recognition algorithms in another paper [25] we do not repeat this here.…”

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confidence: 99%

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Real-time event classification in field sport videos

Kapela

Świetlicka

Rybarczyk

et al. 2015

Signal Processing: Image Communication

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The paper presents a novel approach to real-time event detection in sports broadcasts. We present how the same underlying audio-visual feature extraction algorithm based on new global image descriptors is robust across a range of different sports alleviating the need to tailor it to a particular sport. In addition, we propose and evaluate three different classifiers in order to detect events using these features: a feed-forward neural network, an Elman neural network and a decision tree. Each are investigated and evaluated in terms of their usefulness for real-time event classification. We also propose a ground truth dataset together with an annotation technique for performance evaluation of each classifier useful to others interested in this problem.

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Event Detection and Summarization in Soccer Videos Using Bayesian Network and Copula

Cited by 93 publications

References 27 publications

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

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

A framework for dynamic restructuring of semantic video analysis systems based on learning attention control

Real-time event classification in field sport videos

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