text: 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.
  @cite_0: We focus on the automated analysis of spectator crowd, that is, people watching sport contests alive (in stadiums, amphitheaters etc.), or, more generally, people "watching the activities of an event ['] interested in watching something specific that they came to see" [2]. This scenario differs substantially from the typical crowd analysis setting (e.g. pedestrians): here the dynamics of humans is more constrained, due to the architectural environments in which they are situated; people are expected to stay in a fixed location most of the time, limiting their activities to applaud, support heckle the players or discuss with the neighbors. In this paper, we start facing this challenge by following a social signal processing approach, which grounds computer vision techniques in social theories. More specifically, leveraging on social theories describing expressive bodily conduct, we will show how, by using computer vision techniques, it is possible to distinguish fan groups belonging to different teams by automatically detecting their liveliness in different moments of the match, even when they are merged in the stands. Moreover, we will show how, only by automatically detecting crowd's motions on the stands, it is possible to single out the most salient events of the match, like goals, fouls or shots on goal.
  @cite_9: We propose a new type of crowd analysis, focused on the spectator crowd, that is, people "interested in watching something specific that they came to see" [1]. This scenario applies on stadiums, amphitheaters etc., and shares some aspects with classical crowd monitoring: actually, many people are simultaneously observed, so that per-person analysis is hard; however, here the dynamics of humans is more constrained, due to the architectural environment in which they are situated; specifically, people are expected to stay in a fixed location most of the time, limiting their activities to applaud, support heckle the players or discuss with the neighbors. In this paper, we start facing this challenge by considering hockey matches, locating a videocamera 25-30 meters far from the bleachers, pointing at the crowd: in this scenario, aggregations of spectators that exhibit similar behavior are detected, and the behavior is classified into a set of predefined classes, highlighting the overall excitement. To these aims, in a first step we focus on individual frames, clustering local flow measures into spatial regions. The clustering is then extended by adding the temporal axis into the analysis, looking for non-randomic spatio-temporal clusters; for this purpose, the Lempel-Ziv complexity is considered. This way, choral activities can emerge, indicating for example fan groups belonging to different teams. After this, with the adoption of entropic measures, the degree of excitement of such groups can be quantified.
  @cite_6: 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.
summary: 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.