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. During a live sports event, many sports fans use social media as a part of their viewing experience, reporting on their thoughts on the event as it unfolds. In this work, we use this information stream to semantically annotate live broadcast sports games, using these annotations to select video highlights from the game. We demonstrate that this approach can be used to select highlights specific for fans of each team, and that these clips reflect the emotions of a fan during a game. Further, we describe how these clips differ from those seen on nightly sportscasts. Highlight summarization technology has been studied widely in sports video analysis. In this paper, we propose a highlight summarization system based on replays. First the replay clips in the sports video are extracted as the highlight candidates. Then the features including audio energy and motion activity are employed to rank the arousal level of the replay clips. Finally we model the highlight with the arousal rank to generate summarization. The contribution of this paper concentrates on two aspects. Firstly, Event-Replay (ER) structure is proposed and some new features are employed to represent the arousal levels of ER for general sports video. Secondly a novel highlight model is proposed considering the inter-relation of ERs. The experiments evaluate the rationality of the system. Quantity and quality of sport highlights attract viewers to sport programs and portal sites. Making a good quality highlight requires an experience production team. It is a job that is very hard to automate because of the quality factor. The machines of the current generation are not intelligent enough to make a sound judgment on what establishes a highlight yet. This work focuses on this difficult problem. It introduces a new technique to automate the making of sport highlight that is of acceptable quality. Previous approaches to extracting the highlights employ image and or audio processing technology to capture relevant contexts related to highlight moments, e.g., goal frame to hint at the scoring moment. However, the accuracy of such approaches has not been satisfactory. They generate many false positives that may render the highlights useless. To remedy this, we propose a new approach that combines on-line information retrieval with text extraction using optical character recognition (OCR) - a much more tractable problem than general image processing. The new approach makes better highlights because it is free of false positives by design. In this paper we examine the effectiveness of using a filtered stream of tweets from Twitter to automatically identify events of interest within the video of live sports transmissions. We show that using just the volume of tweets generated at any moment of a game actually provides a very accurate means of event detection, as well as an automatic method for tagging events with representative words from the tweet stream. We compare this method with an alternative approach that uses complex audio-visual content analysis of the video, showing that it provides near-equivalent accuracy for major event detection at a fraction of the computational cost. Using community tweets and discussion also provides a sense of what the audience themselves found to be the talking points of a video. 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. The emergence of wearable devices such as portable cameras and smart glasses makes it possible to record life logging first-person videos. Browsing such long unstructured videos is time-consuming and tedious. This paper studies the discovery of moments of user's major or special interest (i.e., highlights) in a video, for generating the summarization of first-person videos. Specifically, we propose a novel pairwise deep ranking model that employs deep learning techniques to learn the relationship between high-light and non-highlight video segments. A two-stream network structure by representing video segments from complementary information on appearance of video frames and temporal dynamics across frames is developed for video highlight detection. Given a long personal video, equipped with the highlight detection model, a highlight score is assigned to each segment. The obtained highlight segments are applied for summarization in two ways: video time-lapse and video skimming. The former plays the highlight (non-highlight) segments at low (high) speed rates, while the latter assembles the sequence of segments with the highest scores. On 100 hours of first-person videos for 15 unique sports categories, our highlight detection achieves the improvement over the state-of-the-art RankSVM method by 10.5 in terms of accuracy. Moreover, our approaches produce video summary with better quality by a user study from 35 human subjects. Twitter, as a social media is a very popular way of expressing opinions and interacting with other people in the online world. When taken in aggregation tweets can provide a reflection of public sentiment towards events. In this paper, we provide a positive or negative sentiment on Twitter posts using a well-known machine learning method for text categorization. In addition, we use manually labeled (positive negative) tweets to build a trained method to accomplish a task. The task is looking for a correlation between twitter sentiment and events that have occurred. The trained model is based on the Bayesian Logistic Regression (BLR) classification method. We used external lexicons to detect subjective or objective tweets, added Unigram and Bigram features and used TF-IDF (Term Frequency-Inverse Document Frequency) to filter out the features. Using the FIFA World Cup 2014 as our case study, we used Twitter Streaming API and some of the official world cup hashtags to mine, filter and process tweets, in order to analyze the reflection of public sentiment towards unexpected events. The same approach, can be used as a basis for predicting future events. 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 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. 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. Sports video highlight detection is a popular topic. A multi-layer sport event detection framework is described. In the mid-level of this framework, visual and audio keywords are created from low-level features and the original video is converted into a keyword sequence. In the high-level, the temporal pattern of keyword sequences is analyzed by an HMM classifier. The creation of visual and audio keywords can help to bridge the gap between low-level features and high-level semantics. The use of the HMM classifier can automatically find the temporal change character of the event instead of rule based heuristic modeling to map certain keyword sequences into events. Experiments using our model on soccer games produced some promising results In today's fast-paced world, while the number of channels of television programming available is increasing rapidly, the time available to watch them remains the same or is decreasing. Users desire the capability to watch the programs time-shifted (on-demand) and or to watch just the highlights to save time. In this paper we explore how to provide for the latter capability, that is the ability to extract highlights automatically, so that viewing time can be reduced. We focus on the sport of baseball as our initial target—it is a very popular sport, the whole game is quite long, and the exciting portions are few. We focus on detecting highlights using audio-track features alone without relying on expensive-to-compute video-track features. We use a combination of generic sports features and baseball-specific features to obtain our results, but believe that may other sports offer the same opportunity and that the techniques presented here will apply to those sports. We present details on relative performance of various learning algorithms, and a probabilistic framework for combining multiple sources of information. We present results comparing output of our algorithms against human-selected highlights for a diverse collection of baseball games with very encouraging results. In this paper, we present a novel approach towards customized and automated generation of sports highlights from its extracted events and semantic concepts. A recorded sports video is first divided into slots, based on the game progress and for each slot, an importance-based concept and event-selection is proposed to include those in the highlights. Using our approach, we have successfully extracted highlights from recorded video of cricket match. Microblogging sites are the direct platform for the users to express their views. It has been observed from previous studies that people are viable to flaunt their emotions for events (eg. natural catastrophes, sports, academics etc.), for persons (actor actress, sports person, scientist) and for the places they visit. In this study we focused on a sport event, particularly the cricket tournament and collected the emotions of the fans for their favorite players using their tweets. Further, we acquired the stock market performance of the brands which are either endorsing the players or sponsoring the match in the tournament. It has been observed that performance of the player triggers the users to flourish their emotions over social media therefore, we observed correlation between players performance and fans' emotions. Therefore, we found the direct connection between player's performance with brand's behavior on stock market. The recognition of human activities is one of the key problems in video understanding. Action recognition is challenging even for specific categories of videos, such as sports, that contain only a small set of actions. Interestingly, sports videos are accompanied by detailed commentaries available online, which could be used to perform action annotation in a weakly-supervised setting. For the specific case of Cricket videos, we address the challenge of temporal segmentation and annotation of ctions with semantic descriptions. Our solution consists of two stages. In the first stage, the video is segmented into "scenes", by utilizing the scene category information extracted from text-commentary. The second stage consists of classifying video-shots as well as the phrases in the textual description into various categories. The relevant phrases are then suitably mapped to the video-shots. The novel aspect of this work is the fine temporal scale at which semantic information is assigned to the video. As a result of our approach, we enable retrieval of specific actions that last only a few seconds, from several hours of video. This solution yields a large number of labeled exemplars, with no manual effort, that could be used by machine learning algorithms to learn complex actions. 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.