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.
According to Hsieh al @cite_21 , the quantity of comments and re-tweets can represent the most exciting moments in a sport event. A highlight can be determined by analyzing the keywords in the comments and observing if the number of comments and re-tweets passes a certain threshold. Fi a o al @cite_10 uses emotions shared by the spectators during the match via social networks to build a system capable of generating automatic highlight videos of sports match TV broadcasts. Auxiliary sources of information are TV broadcast videos, the audio, the analysis of the movement and manual annotations (when available). The system also allows for the user to query the video to extract specific clips ( attacking plays of a specific team).
@cite_21 @cite_10
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With the widespread use of social networks, sharing emotions has become easy and accessible to anyone with a smartphone or a computer. In this paper, we present a system capable of generating automatic highlight videos of sports match TV broadcasts based on the emotions shared by the spectators during the match, the audio, the analysis of the movement and manual annotations (when available). Our system also allows for the user to query the video to extract specific clips, such as dangerous attacking plays of a certain team. Our preliminary results were encouraging, showing that the creation of these summaries can be successfully done in very short time. Sports highlight generation techniques aim at condensing a full-length video to a significantly shortened version that still preserves the main interesting content of the original video. In this paper, we present the system for automatically generating the highlights from sports TV broadcasts. The proposed system detects exciting clips based on audio features and then classify the individual scenes within the clip into events such as replay, player, referee, spectator, and players gathering. A probabilistic Bayesian belief network based on observed events is used to assign semantic concept-labels to the exciting clips, such as goals, saves, yellow-cards, red-cards, and kicks in soccer video sequences. The labeled clips are selected according to their degree of importance to include in the highlights. We have successfully generated highlights from soccer video sequences. Microblogging as a new form of communication on Internet, has attracted the attention from researchers recently. Relying the real-time and conversational properties of microblogging, its users update their statuses and share experience within their the social network. Those characteristics also make microblogging an important tool for users to share or discuss real world events such as earth quake or sport game. In this paper, we propose a novel and flexible solution to detect and recognize real-time events from sport games based on analyzing the messages posted on microblogging services. We take Twitter as the experiment platform and collect a large-scale dataset of Twitter messages that are called tweets for 18 prominent sport games covering four types of sports in 2011. We also collect corresponding sport videos for those games. The proposed solution applies moving-threshold burst detection on the volume of tweets to detect highlights in sport games. A tf-idf-based weighting method is applied on the tweets within detected highlights for semantic extraction. According to the experiments we perform on the tweet and video datasets, we find that the proposed methods can achieve competent performance in sport event detection and recognition. Besides, our method can find non pre-defined tidbits that are difficult to detect in previous works. 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.