The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and use it to create a real-world system for the editorial aid of golf highlight reels. Our method fuses information from the players' reactions (action recognition such as high-fives and fist pumps), spectators (crowd cheering), and commentator (tone of the voice and word analysis) to determine the most interesting moments of a game. We accurately identify the start and end frames of key shot highlights with additional metadata, such as the player's name and the hole number, allowing personalized content summarization and retrieval. In addition, we introduce new techniques for learning our classifiers with reduced manual training data annotation by exploiting the correlation of different modalities. Our work has been demonstrated at a major golf tournament, successfully extracting highlights from live video streams over four consecutive days.
Sports Highlights Generation. Several methods have been proposed to automatically extract highlights from sports videos based on audio and visual cues. Example approaches include the analysis of replays @cite_15 , crowd cheering @cite_16 , motion features @cite_2 , and closed captioning @cite_3 . More recently, @cite_26 used contextual cues from the environment to understand the excitement levels within a basketball game. Tang and Boring @cite_6 proposed to automatically produce highlights by analyzing social media services such as twitter. @cite_18 developed a method for forecasting sports highlights to achieve more effective coverage of multiple games happening at the same time. Different from existing methods, our proposed approach offers a unique combination of excitement measures to produce highlights, including information from the spectators , the commentator , and the player reaction. In addition, we enable personalized highlight generation or retrieval based on a viewer s favorite players.
@cite_18 @cite_26 @cite_3 @cite_6 @cite_2 @cite_15 @cite_16
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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. 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. 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. We present a novel method for generating sports video summary highlights. Specifically, our method localizes semantically important events in sport programs by detecting slow motion replays of these events, and then generates highlights of these events at multiple levels. In our method, a hidden Markov model (HMM) is used to model slow motion replays, and an inference algorithm is introduced which computes the probability of a slow motion replay segment, and localizes the boundaries of the segment as well. An effective new feature is used in our HMM, based on a moving measure of the number of zero-crossings and the amplitudes of variations over time of video field differences. Furthermore, the method is capable of filtering out slow motion play segments in commercials. As compared with existing methods for video event detection, our method is more generic (ie, domain independent), and has the ability to capture inherently important events.