Flashlight and player detection in fighting sport for video summarization

Benjamas, Nunnapus; Cooharojananone, Nagul; Jaruskulchai, Chuleerat

doi:10.1109/iscit.2005.1566888

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…In order to model the semantics of video frames, object based and event based models in video summarization have been proposed for sports events [20]. For example, object features are used to detect referees and the penalty box in soccer games [21], and players detection in fighting sports video was conducted to determine key-frames based on the distance between players [22], and a video understanding scheme for baseball programs can be established similarly [23].…”

Section: Related Workmentioning

confidence: 99%

Key-frame extraction of wildlife video based on semantic context modeling

Yong¹,

Deng²,

Purvis³

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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In recent work on image and video retrieval there seems to be a shift of focus from low-level feature extraction to producing high-level semantic representation of scenes. This paper presents a framework that produces semantic context features from video frames which are then employed for keyframe extraction. Working with wildlife video frames, the framework starts with image segmentation, followed by low-level feature extraction and classification of the image blocks extracted from image segments. Based on the image block labels in the neighbourhood a co-occurrence matrix is then constructed to represent the semantic context of the scene. The semantic cooccurrence matrices then undergo binarization and principal component analysis for dimension reduction, forming the feature vectors used in a one-class classifier that extracts the key-frames. Experiments show that the utilization of high-level semantic features result in better key-frame extraction when compared with methods using low-level features only.

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Section: Related Workmentioning

confidence: 99%

Key-frame extraction of wildlife video based on semantic context modeling

Yong¹,

Deng²,

Purvis³

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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“…Katsiouli et al [6] used subtitles for documentary classification. They performed categorization by using the WordNet lexical database and WordNet Domains [14] and applied natural language processing techniques on subtitles. They predefined documentary categories as geography, history, animals, politics, religion, sports, music, accidents, art, science, transportation, technology, people and war.…”

Section: Related Work In Video Categorizationmentioning

confidence: 99%

Automatic categorization and summarization of documentaries

Demirtas

Cicekli

Çiçekli

2010

Journal of Information Science

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In this paper, we propose automatic categorization and summarization of documentaries using subtitles of videos. We propose two methods for video categorization. The first makes unsupervised categorization by applying natural language processing techniques on video subtitles and uses the WordNet lexical database and WordNet domains. The second has the same extraction steps but uses a learning module to categorize. Experiments with documentary videos give promising results in discovering the correct categories of videos. We also propose a video summarization method using the subtitles of videos and text summarization techniques. Significant sentences in the subtitles of a video are identified using these techniques and a video summary is then composed by finding the video parts corresponding to these summary sentences. © 2010 The Author(s)

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“…Benjamas et al [9] used color histogram comparison to detect shot boundaries. Sandra E.F. deAvila et al [10] generated summaries based on color attributes and visual features.…”

Section: Introductionmentioning

confidence: 99%

Lawn Tennis Video Summarization based on Audiovisual and Text Feature Analysis

Kanade¹,

Patil²

2012

IJCA

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In this paper, a new video summarization approach for lawn tennis video is presented. The proposed method uses frame color histogram to classify video into play field color shots (PFCS) and non play field color shots (NPFCS). Play field color shots are the segments of interest and used to recognize the tournament class. A dominant colored frame from every PFCS is extracted as a salient frame. Our approach also employs dominant value of PFCS volume and energy. For the each PFCS dominant audio energy value is computed and corresponding key frame is extracted. On-screen text presented in play field color shots provides important semantic information so the last frame of the shot is extracted as key caption. Three key frames based on audio, visual and text information conveyed in every PFCS are extracted and summary of lawn tennis video is created. These key frames of every PFCS serve as accurate localization of the events on the lawn tennis field. Experiments are performed on lawn tennis videos to confirm the efficiency of the proposed method. General TermsShot boundary detection, video summarization KeywordsShot Classification, Color histogram, key frame, play field color shots, non play field color shots, Video Summarization.

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Flashlight and player detection in fighting sport for video summarization

Cited by 11 publications

References 7 publications

Key-frame extraction of wildlife video based on semantic context modeling

Key-frame extraction of wildlife video based on semantic context modeling

Automatic categorization and summarization of documentaries

Lawn Tennis Video Summarization based on Audiovisual and Text Feature Analysis

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