2,921 research outputs found
Video summarisation: A conceptual framework and survey of the state of the art
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 field sports video using a petri-net of audio-visual concepts
The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports
video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework
Leveraging Contextual Cues for Generating Basketball Highlights
The massive growth of sports videos has resulted in a need for automatic
generation of sports highlights that are comparable in quality to the
hand-edited highlights produced by broadcasters such as ESPN. Unlike previous
works that mostly use audio-visual cues derived from the video, we propose an
approach that additionally leverages contextual cues derived from the
environment that the game is being played in. The contextual cues provide
information about the excitement levels in the game, which can be ranked and
selected to automatically produce high-quality basketball highlights. We
introduce a new dataset of 25 NCAA games along with their play-by-play stats
and the ground-truth excitement data for each basket. We explore the
informativeness of five different cues derived from the video and from the
environment through user studies. Our experiments show that for our study
participants, the highlights produced by our system are comparable to the ones
produced by ESPN for the same games.Comment: Proceedings of ACM Multimedia 201
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Temporal hybridity: Mixing live video footage with instant replay in real time
Copyright @ 2010 ACMIn this paper we explore the production of streaming media that involves live and recorded content. To examine this, we report on how the production practices and process are conducted through an empirical study of the production of live television, involving the use of live and non-live media under highly time critical conditions. In explaining how this process is managed both as an individual and collective activity, we develop the concept of temporal hybridity to
explain the properties of these kinds of production system and show how temporally separated media are used, understood and coordinated. Our analysis is examined in
the light of recent developments in computing technology and we present some design implications to support amateur video production.The research was partly made possible by a grant from the Swedish Governmental Agency for Innovation Systems to the Mobile Life VinnExcellence Center, in partnership with
SonyEricsson, Ericsson, Microsoft Research, Nokia Research, TeliaSonera and the City of Stockholm
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Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors
In todayās fast paced world, as the number of stations of television programming offered is increasing rapidly, time accessible to watch them remains same or decreasing. Sports videos are typically lengthy and they appeal to a massive crowd. Though sports video is lengthy, most of the viewerās desire to watch specific segments of the video which are fascinating, like a home-run in a baseball or goal in soccer i.e., users prefer to watch highlights to save time. When associated to the entire span of the video, these segments form only a minor share. Hence these videos need to be summarized for effective presentation and data management. This thesis explores the ability to extract highlights automatically using MPEG-7 features and hidden Markov model (HMM), so that viewing time can be reduced. Video is first segmented into scene shots, in which the detection of the shot is the fundamental task. After the video is segmented into shots, extraction of key frames allows a suitable representation of the whole shot. Feature extraction is crucial processing step in the classification, video indexing and retrieval system. Frame features such as color, motion, texture, edges are extracted from the key frames. A baseball highlight contains certain types of scene shots and these shots follow a particular transition pattern. The shots are classified as close-up, out-field, base and audience. I first try to identify the type of the shot using low level features extracted from the key frames of each shot. For the identification of the highlight I use the hidden Markov model using the transition pattern of the shots in time domain. Experimental results suggest that with reasonable accuracy highlights can be extracted from the video
Multimodal framework based on audioāvisual features for summarisation of cricket videos
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166171/1/ipr2bf02094.pd
Real-time event detection in field sport videos
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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