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.
Rui al @cite_17 presents a method that uses audio signals to build video highlights for baseball games. It analyzes the speech of the match announcer, both audio amplitude and voice tone, to estimate whether the announcer is being excited or not. In addition, the ambient sound from the surrounding environment and the audience are also taken into considerations. Built on this work, Xiong al @cite_13 handpicked the highlight events and analyzed the environment and audience sounds at each of those highlight events. They discovered that there exists a strong correlation between loud and buzzing noise and some major highlight events. This correlation exists in all the three sports being analyzed: baseball, golf, and soccer.
@cite_13 @cite_17
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We have developed a unified framework to extract highlights from three sports - baseball, golf and soccer - by detecting some of the common audio events that are directly indicative of highlights. We used MPEG-7 audio features and entropic prior hidden Markov models (HMM) for feature extraction and classification, respectively, to recognize these common audio events. Together with pre- and post-processing techniques using general sports knowledge, we have been able to generate promising results dealing with an audio track that is dominated by audio mixtures and noisy background. We propose to use a visual object (e.g., the baseball catcher) detection algorithm to find local, semantic objects in video frames in addition to an audio classification algorithm to find semantic audio objects in the audio track for sports highlights extraction. The highlight candidates are then further grouped into finer-resolution highlight segments, using color or motion information. During the grouping phase, many of the false alarms can be correctly identified and eliminated. Our experimental results with baseball, soccer and golf video are promising.