• Title/Summary/Keyword: Highlight Video

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Creation of Soccer Video Highlight Using The Structural Features of Caption (자막의 구조적 특징을 이용한 축구 비디오 하이라이트 생성)

  • Huh, Moon-Haeng;Shin, Seong-Yoon;Lee, Yang-Weon;Ryu, Keun-Ho
    • The KIPS Transactions:PartD
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    • v.10D no.4
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    • pp.671-678
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    • 2003
  • A digital video is usually very long temporally. requiring large storage capacity. Therefore, users want to watch pre-summarized video before they watch a large long video. Especially in the field of sports video, they want to watch a highlight video. Consequently, highlight video is used that the viewers decide whether it is valuable for them to watch the video or not. This paper proposes how to create soccer video highlight using the structural features of the caption such as temporal and spatial features. Caption frame intervals and caption key frames are extracted by using those structural features. And then, highlight video is created by using scene relocation, logical indexing and highlight creation rule. Finally. retrieval and browsing of highlight and video segment is performed by selection of item on browser.

Subdivision Ensemble Model for Highlight Detection (하이라이트 검출을 위한 구간 분할 앙상블 모델)

  • Lee, Hansol;Lee, Gyemin
    • Journal of Broadcast Engineering
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    • v.25 no.4
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    • pp.620-628
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    • 2020
  • Automatically predicting video highlight is an important task for media industry and streaming platform providers to save time and cost of manual video editing process. We propose a new ensemble model that combines multiple highlight detectors with each focusing on different parts of highlight events. Therefore, our model can capture more information-rich sections of events. Furthermore, the proposed model can extract improved features for highlight detection particularly when the train video set is small. We evaluate our model on e-sports and baseball videos.

Soccer Video Highlight Building Algorithm using Structural Characteristics of Broadcasted Sports Video (스포츠 중계 방송의 구조적 특성을 이용한 축구동영상 하이라이트 생성 알고리즘)

  • 김재홍;낭종호;하명환;정병희;김경수
    • Journal of KIISE:Software and Applications
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    • v.30 no.7_8
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    • pp.727-743
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    • 2003
  • This paper proposes an automatic highlight building algorithm for soccer video by using the structural characteristics of broadcasted sports video that an interesting (or important) event (such as goal or foul) in sports video has a continuous replay shot surrounded by gradual shot change effect like wipe. This shot editing rule is used in this paper to analyze the structure of broadcated soccer video and extracts shot involving the important events to build a highlight. It first uses the spatial-temporal image of video to detect wipe transition effects and zoom out/in shot changes. They are used to detect the replay shot. However, using spatial-temporal image alone to detect the wipe transition effect requires too much computational resources and need to change algorithm if the wipe pattern is changed. For solving these problems, a two-pass detection algorithm and a pixel sub-sampling technique are proposed in this paper. Furthermore, to detect the zoom out/in shot change and replay shots more precisely, the green-area-ratio and the motion energy are also computed in the proposed scheme. Finally, highlight shots composed of event and player shot are extracted by using these pre-detected replay shot and zoom out/in shot change point. Proposed algorithm will be useful for web services or broadcasting services requiring abstracted soccer video.

Video Automatic Editing Method and System based on Machine Learning (머신러닝 기반의 영상 자동 편집 방법 및 시스템)

  • Lee, Seung-Hwan;Park, Dea-woo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.235-237
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    • 2022
  • Video content is divided into long-form video content and short-form video content according to the length. Long form video content is created with a length of 15 minutes or longer, and all frames of the captured video are included without editing. Short-form video content can be edited to a shorter length from 1 minute to 15 minutes, and only some frames from the frames of the captured video. Due to the recent growth of the single-person broadcasting market, the demand for short-form video content to increase viewers is increasing. Therefore, there is a need for research on content editing technology for editing and generating short-form video content. This study studies the technology to create short-form videos of main scenes by capturing images, voices, and motions. Short-form videos of key scenes use a pre-trained highlight extraction model through machine learning. An automatic video editing system and method for automatically generating a highlight video is a core technology of short-form video content. Machine learning-based automatic video editing method and system research will contribute to competitive content activities by reducing the effort and cost and time invested by single creators for video editing

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Soccer Video Highlight Building Algorithm using Video Characteristic of Broadcasted Sports Video (스포츠 중계 방송의 특성을 이용한 축구동영상 하이라이트 생성 알고리즘)

  • 김재홍;낭종호;하명환;정병희;김경수
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.10d
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    • pp.196-198
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    • 2002
  • 본 논문에서는 축구 동영상에서 스포츠 중계방송의 특성을 이용하여 자동적으로 하이라이트(Highlight)를 추출하는 새로운 방법을 제안하고 실험을 통하여 그 유용성을 증명한다. 일반적으로 스포츠 중계 방송에서는 중요한 이벤트(골, 반칙)가 발생하면 그 장면 을 다시 느린속도의 리플레이(Replay) 화면으로 보여주고, 리플레이가 시작되고 끝날 때 Wipe와 같은 점진적인 화면 전환 기법을 사용하는 특성을 가지고 있다. 본 논문에서는 이러한 스포츠 중계방송의 특징을 이용하여 Wipe검출, Replay검출 및 Zoom-In/Out 전환 검출을 이용하여 전체 축구 동영상에서 하이라이트만을 추출하는 방법을 제안한다.

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Video Highlight Prediction Using Multiple Time-Interval Information of Chat and Audio (채팅과 오디오의 다중 시구간 정보를 이용한 영상의 하이라이트 예측)

  • Kim, Eunyul;Lee, Gyemin
    • Journal of Broadcast Engineering
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    • v.24 no.4
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    • pp.553-563
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    • 2019
  • As the number of videos uploaded on live streaming platforms rapidly increases, the demand for providing highlight videos is increasing to promote viewer experiences. In this paper, we present novel methods for predicting highlights using chat logs and audio data in videos. The proposed models employ bi-directional LSTMs to understand the contextual flow of a video. We also propose to use the features over various time-intervals to understand the mid-to-long term flows. The proposed Our methods are demonstrated on e-Sports and baseball videos collected from personal broadcasting platforms such as Twitch and Kakao TV. The results show that the information from multiple time-intervals is useful in predicting video highlights.

A Personal Videocasting System with Intelligent TV Browsing for a Practical Video Application Environment

  • Kim, Sang-Kyun;Jeong, Jin-Guk;Kim, Hyoung-Gook;Chung, Min-Gyo
    • ETRI Journal
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    • v.31 no.1
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    • pp.10-20
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    • 2009
  • In this paper, a video broadcasting system between a home-server-type device and a mobile device is proposed. The home-server-type device can automatically extract semantic information from video contents, such as news, a soccer match, and a baseball game. The indexing results are utilized to convert the original video contents to a digested or arranged format. From the mobile device, a user can make recording requests to the home-server-type devices and can then watch and navigate recorded video contents in a digested form. The novelty of this study is the actual implementation of the proposed system by combining the actual IT environment that is available with indexing algorithms. The implementation of the system is demonstrated along with experimental results of the automatic video indexing algorithms. The overall performance of the developed system is compared with existing state-of-the-art personal video recording products.

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Salient Region Detection Algorithm for Music Video Browsing (뮤직비디오 브라우징을 위한 중요 구간 검출 알고리즘)

  • Kim, Hyoung-Gook;Shin, Dong
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.2
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    • pp.112-118
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    • 2009
  • This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

Dynamic Summarization and Summary Description Scheme for Efficient Video Browsing (효율적인 비디오 브라우징을 위한 동적 요약 및 요약 기술구조)

  • 김재곤;장현성;김문철;김진웅;김형명
    • Journal of Broadcast Engineering
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    • v.5 no.1
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    • pp.82-93
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    • 2000
  • Recently, the capability of efficient access to the desired video content is of growing importance because more digital video data are available at an increasing rate. A video summary abstracting the gist from the entirety enables the efficient browsing as well as the fast skimming of the video contents. In this paper, we discuss a novel dynamic summarization method based on the detection of highlights which represent semantically significant content and the description scheme (DS) proposed to MPEG-7 aiming to provide summary description. The summary DS proposed to MPEG-7 allows for efficient navigation and browsing to the contents of interest through the functionalities of multi-level highlights, hierarchical browsing and user-customized summarization. In this paper, we also show the validation and the usefulness of the methodology for dynamic summarization and the summary DS in real applications with soccer video sequences.

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Video Highlight Prediction Using GAN and Multiple Time-Interval Information of Audio and Image (오디오와 이미지의 다중 시구간 정보와 GAN을 이용한 영상의 하이라이트 예측 알고리즘)

  • Lee, Hansol;Lee, Gyemin
    • Journal of Broadcast Engineering
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    • v.25 no.2
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    • pp.143-150
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    • 2020
  • Huge amounts of contents are being uploaded every day on various streaming platforms. Among those videos, game and sports videos account for a great portion. The broadcasting companies sometimes create and provide highlight videos. However, these tasks are time-consuming and costly. In this paper, we propose models that automatically predict highlights in games and sports matches. While most previous approaches use visual information exclusively, our models use both audio and visual information, and present a way to understand short term and long term flows of videos. We also describe models that combine GAN to find better highlight features. The proposed models are evaluated on e-sports and baseball videos.