Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience. A big effort is spent in designing techniques which find automatically highlights, in order to automatize the otherwise manual editing process. Most of the state-of-the-art approaches try to solve the problem by training a classifier using the information extracted on the tv-like framing of players playing on the game pitch, learning to detect game actions which are labeled by human observers according to their perception of highlight. Obviously, this is a long and expensive work. In this paper, we reverse the paradigm: instead of looking at the gameplay, inferring what could be exciting for the audience, we directly analyze the audience behavior, which we assume is triggered by events happening during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to extract visual features from cropped video recordings of the supporters that are attending the event. Outputs of the crops belonging to the same frame are then accumulated to produce a value indicating the Highlight Likelihood (HL) which is then used to discriminate between positive (i.e. when a highlight occurs) and negative samples (i.e. standard play or time-outs). Experimental results on a public dataset of ice-hockey matches demonstrate the effectiveness of our method and promote further research in this new exciting direction.
Peng al @cite_6 propose the Interest Meter (IM), a system able to measure user’s interest and thus use it to conduct video summarization. The IM takes account attention states ( eye movement, blink, and head motion) and emotion states ( facial expression). These features are then fused together by a fuzzy fusion scheme that outputs a quantitative interest score, determine interesting parts of videos, and finally concatenate them as video summaries. @cite_9 , Conigliaro al use motion cues ( optical flow intensity and direction entropy) to estimate the excitement level of audience of a team sport event and to identify groups of supporters of different teams. @cite_0 , these features are used to identify highlights in team sport events using mean shift clustering.
@cite_0 @cite_9 @cite_6
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In this paper, we propose the Interest Meter (IM), a system making the computer conscious of user's reactions to measure user's interest and thus use it to conduct video summarization. The IM takes account of users' spontaneous reactions when they view videos. To estimate user's viewing interest, quantitative interest measures are devised based on the perspectives of attention and emotion. For estimating attention states, variations of user's eye movement, blink, and head motion are considered. For estimating emotion states, facial expression is recognized as positive or neural emotion. By combining characteristics of attention and emotion by a fuzzy fusion scheme, we transform users' viewing behaviors into quantitative interest scores, determine interesting parts of videos, and finally concatenate them as video summaries. Experimental results show that the proposed concept editing by viewing works well and may provide a promising direction to consider the human factor in video summarization. This paper addresses the challenge of automatically extracting the highlights from sports TV broadcasts. In particular, we are interested in finding a generic method of highlights extraction, which does not require the development of models for the events that are thought to be interpreted by the users as highlights. Instead, we search for highlights in those video segments that are expected to excite the users most. It is namely realistic to assume that a highlighting event induces a steady increase in a user's excitement, as compared to other, less interesting events. We mimic the expected variations in a user's excitement by observing the temporal behavior of selected audiovisual low-level features and the editing scheme of a video. Relations between this noncontent information and the evoked excitement are drawn partly from psychophysiological research and partly from analyzing the live-video directing practice. The expected variations in a user's excitement are represented by the excitement time curve, which is, subsequently, filtered in an adaptive way to extract the highlights in the prespecified total length and in view of the preferences regarding the highlights strength: extraction can namely be performed with variable sensitivity to capture few "strong" highlights or more "less strong" ones. We evaluate and discuss the performance of our method on the case study of soccer TV broadcasts. During a live sports event, many sports fans use social media as a part of their viewing experience, reporting on their thoughts on the event as it unfolds. In this work, we use this information stream to semantically annotate live broadcast sports games, using these annotations to select video highlights from the game. We demonstrate that this approach can be used to select highlights specific for fans of each team, and that these clips reflect the emotions of a fan during a game. Further, we describe how these clips differ from those seen on nightly sportscasts.