Automatically generating a summary of sports video poses the challenge of detecting interesting moments, or highlights, of a game. Traditional sports video summarization methods leverage editing conventions of broadcast sports video that facilitate the extraction of high-level semantics. However, user-generated videos are not edited, and thus traditional methods are not suitable to generate a summary. In order to solve this problem, this work proposes a novel video summarization method that uses players' actions as a cue to determine the highlights of the original video. A deep neural network-based approach is used to extract two types of action-related features and to classify video segments into interesting or uninteresting parts. The proposed method can be applied to any sports in which games consist of a succession of actions. Especially, this work considers the case of Kendo (Japanese fencing) as an example of a sport to evaluate the proposed method. The method is trained using Kendo videos with ground truth labels that indicate the video highlights. The labels are provided by annotators possessing different experience with respect to Kendo to demonstrate how the proposed method adapts to different needs. The performance of the proposed method is compared with several combinations of different features, and the results show that it outperforms previous summarization methods.
Existing work also proposed several methods that are not based on heuristics. These methods leverage variations between scenes that are found in broadcast video (e.g., the close-up in a goal celebration in soccer). @cite_42 detected intensity variations in color frames to segment relevant events to summarize broadcast videos of soccer, basketball, and tennis. @cite_9 detected the extrema in the optical flow of a video to extract the frames with the highest action content and construct a summary for broadcast rugby video. These methods can be more generally applied to broadcast videos, but they lack high-level semantics, and thus the extracted scenes do not always correspond to the highlights of the game.
@cite_9 @cite_42
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We propose a general framework for event detection and summary generation in broadcast sports video. Under this framework, important events in a class of sports are modeled by "plays", defined according to the semantics of the particular sport and the conventional broadcasting patterns. We propose both deterministic and probabilistic approaches for the detection of the plays. The detected plays are concatenated to generate a compact, time compressed summary of the original video. Such a summary is complete in the sense that it contains every meaningful action of the underlying game, and it also servers as a much better starting point for higher-level summarization and or analysis than the original video does. We provide experimental results on American football, baseball, and sumo wrestling. In this paper, we propose a novel approach for detecting highlights in sports videos. The videos are temporally decomposed into a series of events based on an unsupervised event discovery and detection framework. The framework solely depends on easy-to-extract low-level visual features such as color histogram (CH) or histogram of oriented gradients (HOG), which can potentially be generalized to different sports. The unigram and bigram statistics of the detected events are then used to provide a compact representation of the video. The effectiveness of the proposed representation is demonstrated on cricket video classification: Highlight vs. Non-Highlight for individual video clips (7000 training and 7000 test instances). We achieve a low equal error rate of 12.1 using event statistics based on CH and HOG features. 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.