Soccer video summarization using enhanced logo detection

Eldib, Mohamed; Zaid, Bassam Sherif Abou; Zawbaa, Hossam M.; El-Zahar, Mohamed H.; El-Saban, Motaz

doi:10.1109/icip.2009.5413649

Cited by 30 publications

(17 citation statements)

References 5 publications

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“…Besides the two methods, for the soccer game videos of Soc09 and Soc10, we also apply the cinematic template [5] and the logo based [9] techniques, and compare them with our bookmarking methods. The cinematic technique exploits the high-level semantics in soccer game videos to detect the goal events, where the detection is based on four semantic rules including 30 ∼ 120 seconds of break duration, occurrence of close-up/out of field shot, existence of slow-motion replay, and the relative position of the replay shot.…”

Section: Methodology and Metricsmentioning

confidence: 99%

“…Ekin et al [5] proposed a cinematic template technique for detecting highlights in soccer game videos. Eldib et al [9] summarizes the soccer game highlights by detecting the logo image appearing before and after the events. Tong et al [7] exploited replay scenes to identify and locate highlights in sport videos.…”

Section: Related Workmentioning

confidence: 99%

“…Many highlight event detection and summarization techniques have been proposed for video bookmarking in recent years (e.g., [5][6][7][8][9]). However, these methods are generally based on analyzing semantic features of the video contents, such as the key frames, motions, replays, textures, etc.…”

Section: Introductionmentioning

confidence: 99%

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Extracting viewer interests for automated bookmarking in video-on-demand services

Zhao

Tian

Liu

2014

Front. Comput. Sci.

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Video-on-demand (VoD) services have become popular on the Internet in recent years. In VoD, it is challenging to support the VCR functionality, especially the jumps, while maintaining a smooth streaming quality. Previous studies propose to solve this problem by predicting the jump target locations and prefetching the contents. However, through our analysis on traces from a real-world VoD service, we find that it would be fundamentally difficult to improve a viewer's VCR experience by simply predicting his future jumps, while ignoring the intentions behind these jumps.Instead of the prediction-based approach, in this paper, we seek to support the VCR functionality by bookmarking the videos. There are two key techniques in our proposed methodology. First, we infer and differentiate viewers' intentions in VCR jumps by decomposing the interseek times, using an expectation-maximization (EM) algorithm, and combine the decomposed inter-seek times with the VCR jumps to compute a numerical interest score for each video segment. Second, based on the interest scores, we propose an automated video bookmarking algorithm. The algorithm employs the time-series change detection techniques of CUSUM and MB-GT, and bookmarks videos by detecting the abrupt changes on their interest score sequences. We evaluate our proposed techniques using real-world VoD traces from dozens of videos. Experimental results suggest that with our methods, viewers' interests within a video can be precisely extracted, and we can position bookmarks on the video's highlight events accurately. Our proposed video bookmarking methodology does not require any knowledge on video type, contents, and semantics, and can be applied on various types of videos.

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Section: Methodology and Metricsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Extracting viewer interests for automated bookmarking in video-on-demand services

Zhao

Tian

Liu

2014

Front. Comput. Sci.

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“…[3] proposes a framebased semantic analysis for each input frame. [2] extracts mid-level features from shots and feeds them into a rulebased classifier. Recent works such as [12] segment videos into play-break(PB) sequences, which is considered as the semantically-meaningful segments for sports videos [13].…”

Section: Introductionmentioning

confidence: 99%

Event detection in soccer videos using shot focus identification

Zhao

Jiang

et al. 2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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This paper presents an automatic event detection system fusing low and mid level features for soccer videos. We first employ an improved approach for Shot Boundary Detection with color and our mean-gradient feature. Then we classify the shots into two view types. We also perform a templatebased replay detection for each shot. Play-break sequences are then generated using a rule-based method. We devise a novel method to locate the spatial position of event occurrence accurately using shot focus identification and extract other features from the play-break sequences. These descriptors are finally fed into a multi-kernel SVM classifier. Experiments tested on various matches demonstrate the effectiveness of our method.

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“…Much research has been carried out into improving those algorithms by using various different techniques from histogram intersections to hidden Markov models [1,5]. These techniques require a huge post-processing effort and do not facilitate anything close to real-time summarization.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Personalized and automatic social summarization of events in video

Hannon

McCarthy

Lynch³

et al. 2011

Proceedings of the 16th International Conference on Intelligent User Interfaces

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Social services like Twitter are increasingly used to provide a conversational backdrop to real-world events in real-time. Sporting events are a good example of this and this year, millions of users tweeted their comments as they watched the World Cup matches from around the world. In this paper, we look at using these time-stamped opinions as the basis for generating video highlights for these soccer matches. We introduce the PASSEV system and describe and evaluate two basic summarization approaches.

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Soccer video summarization using enhanced logo detection

Cited by 30 publications

References 5 publications

Extracting viewer interests for automated bookmarking in video-on-demand services

Extracting viewer interests for automated bookmarking in video-on-demand services

Event detection in soccer videos using shot focus identification

Personalized and automatic social summarization of events in video

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