1,048 research outputs found

    Extensible Detection and Indexing of Highlight Events in Broadcasted Sports Video

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    Content-based indexing is fundamental to support and sustain the ongoing growth of broadcasted sports video. The main challenge is to design extensible frameworks to detect and index highlight events. This paper presents: 1) A statistical-driven event detection approach that utilizes a minimum amount of manual knowledge and is based on a universal scope-of-detection and audio-visual features; 2) A semi-schema-based indexing that combines the benefits of schema-based modeling to ensure that the video indexes are valid at all time without manual checking, and schema-less modeling to allow several passes of instantiation in which additional elements can be declared. To demonstrate the performance of the events detection, a large dataset of sport videos with a total of around 15 hours including soccer, basketball and Australian football is used

    Enhancing fan experience during live sports broadcasts through second screen applications

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    When sports fans attend live sports events, they usually engage in social experiences with friends, family members and other fans at the venue sharing the same affiliation. However, fans watching the same event through a live television broadcast end up not feeling so emotionally connected with the athletes and other fans as they would if they were watching it live, together with thousands of other fans. With this in mind, we seek to create mobile applications that deliver engaging social experiences involving remote fans watching live broadcasted sports events. Taking into account the growing use of mobile devices when watching TV broadcasts, these mobile applications explore the second screen concept, which allows users to interact with content that complements the TV broadcast. Within this context, we present a set of second screen application prototypes developed to test our concepts, the corresponding user studies and results, as well as suggestions on how to apply the prototypes’ concepts not only in different sports, but also during TV shows and electronic sports. Finally, we also present the challenges we faced and the guidelines we followed during the development and evaluation phases, which may give a considerable contribution to the development of future second screen applications for live broadcasted events

    Video summarisation: A conceptual framework and survey of the state of the art

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    This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users

    Semantic Analysis of High-definition MPEG-2 Soccer Video Using Bayesian Network

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    近年,インターネットのブロードバンド化に伴い,映像配信が普及し,また,地上デジタル放送や,BS・CSデジタル放送などの衛星放送により,ユーザが試聴できる番組の数が急増してきている.パソコンやレコーダのハードディスクの容量も増え,大量の番組(コンテンツ)を保存することが可能となったが,その反面,膨大な映像データの中から,視聴者の求めるシーンを素早く検索する技術の必要性がこれまでにも増して高まって来ている.本研究はサッカー映像のリプレーシーンとゴール付近のハイライトシーンの検出方法を提案する.シーンの検出には,MPEG-2エンコーダによって圧縮されたハイビジョンサッカー映像から抽出した特徴量とハイライトシーンとの間の因果関係をベイジアンネットワークで記述する手法を用いる.ベイジアンネットワークを用いることにより,抽出された特徴量からハイライトシーンの発生を確率的に推論することが可能になる.すでにベイジアンネットワークを用いたサッカー映像のハイライトシーンの検出法は提案されているが,それらの方法では,フレーム毎に画素単位でさまざまな画像処理を映像に施すことによって求めた特徴量を利用している.そのため,画面が大きくなると計算コストも大きくなるので,リアルタイム処理には専用の処理装置が必要になる.本研究で提案する方法はMPEG-2圧縮データに含まれている符号化パラメータから特徴量を計算するので,従来法に比べて計算量が少なく,ハイビジョンなどの高解像度映像であっても,通常のPCを用いてリアルタイム処理が可能である.また,従来法では各種シーンに対してベイジアンネットワークが提案されているが,いずれも,ネットワークモデル中のシーンに関わるイベントがすべてフレーム単位で定義されている.例えば,従来法のゴールシーンに関わる,ゴールゲートの出現,観客の声,リプレーの発生等のイベントは全てフレーム単位で数えている.しかし,各イベントの開始・終了フレームを明確に判定する手法が明らかにされておらず,場合によっては人の手で行わなう必要がある.そのため,ベイジアンネットワークを学習する時に、各種イベントの時間帯の与え方に誤差が含まれる可能性がある.さらに、テストビデオから,シーン検出する時,シーンの始終時間帯の検出も困難である.本研究の提案手法では,まず,MPEG-2圧縮データから直接抽出した符号化パラメータの特徴的な変化から,カメラの切り換えに伴う画面の切り替るカット点を検出し,隣接する二つのカット点間をショットとして定義する.さらに各ショットの特徴量を調べることにより,ショットをいくつかのイベントクラスに分類する.さらに,シーンをある特徴的なイベントの発生として捉えることにより,シーンの検出を行う.本手法では,各イベントの開始・終了時刻をショットのカット点によって明確に与えることができることができ,しかもMPEG-2圧縮データから自動的に求めることが可能である.提案方式の性能評価のために,実際のビデオデータを使用した検出実験を行ったところ,ゴール付近で起こるイベントシーンの再現率が86.17%,適合率90.76%,またリプレーシーンの再現率が81.00%, 適合率92.57%という検出結果が得られた.一方,従来法の検出結果では,同一のビデオデータではないが,ゴール付近で起こるイベントシーンの再現率71.1%,適合率89.8%であり,提案方式のほうが従来法に比べ,再現率,適合率ともに上回り,とくに再現率の向上が顕著である.以上のことより,提案法の有効性が確認された.電気通信大学201

    Personalized retrieval of sports video

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    Real-time event classification in field sport videos

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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

    Deep Unsupervised Multi-View Detection of Video Game Stream Highlights

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    We consider the problem of automatic highlight-detection in video game streams. Currently, the vast majority of highlight-detection systems for games are triggered by the occurrence of hard-coded game events (e.g., score change, end-game), while most advanced tools and techniques are based on detection of highlights via visual analysis of game footage. We argue that in the context of game streaming, events that may constitute highlights are not only dependent on game footage, but also on social signals that are conveyed by the streamer during the play session (e.g., when interacting with viewers, or when commenting and reacting to the game). In this light, we present a multi-view unsupervised deep learning methodology for novelty-based highlight detection. The method jointly analyses both game footage and social signals such as the players facial expressions and speech, and shows promising results for generating highlights on streams of popular games such as Player Unknown's Battlegrounds

    Real-time event detection in field sport videos

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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 score
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