We present a parameterized approach to produce personalized variable length summaries of soccer matches. Our approach is based on temporally segmenting the soccer video into 'plays', associating a user-specifiable 'utility' for each type of play and using 'bin-packing' to select a subset of the plays that add up to the desired length while maximizing the overall utility (volume in bin-packing terms). Our approach systematically allows a user to override the default weights assigned to each type of play with individual preferences and thus see a highly personalized variable length summarization of soccer matches. We demonstrate our approach based on the output of an end-to-end pipeline that we are building to produce such summaries. Though aspects of the overall end-to-end pipeline are human assisted at present, the results clearly show that the proposed approach is capable of producing semantically meaningful and compelling summaries. Besides the obvious use of producing summaries of superior league matches for news broadcasts, we anticipate our work to promote greater awareness of the local matches and junior leagues by producing consumable summaries of them.
Sports video summarization in general has seen considerable traction in recent times -- player and ball motion tracking @cite_6 , highlight generation using audio visual cues @cite_19 @cite_1 etc. are being actively pursued in constrained settings. A recent and an interesting approach to generate basketball highlights @cite_21 leverages multimodal context cues for video summarization. While the system asserts good results in indoor basketball settings, replicating same to the outdoor settings would be a challenge in the absence of high quality broadcast data complemented with player and play-by-play stats. Local soccer matches or junior leagues are devoid of such elaborate setups and we only have video data in such settings. We thus propose a generic parameterized approach based on visual motion' cues to generate highlights of the soccer matches.
@cite_19 @cite_21 @cite_1 @cite_6
2141858776 2010405190 2949078144 2221079897 2120748909 2235038879 1930032979 2017448731 2467794422 2963675021 2123807687 1954922063 2155853791 2086598255 2143800062 2602920898 2083545271 2895153665 2062400178 2105636511
We propose a fully automatic and computationally efficient framework for analysis and summarization of soccer videos using cinematic and object-based features. The proposed framework includes some novel low-level processing algorithms, such as dominant color region detection, robust shot boundary detection, and shot classification, as well as some higher-level algorithms for goal detection, referee detection, and penalty-box detection. The system can output three types of summaries: i) all slow-motion segments in a game; ii) all goals in a game; iii) slow-motion segments classified according to object-based features. The first two types of summaries are based on cinematic features only for speedy processing, while the summaries of the last type contain higher-level semantics. The proposed framework is efficient, effective, and robust. It is efficient in the sense that there is no need to compute object-based features when cinematic features are sufficient for the detection of certain events, e.g., goals in soccer. It is effective in the sense that the framework can also employ object-based features when needed to increase accuracy (at the expense of more computation). The efficiency, effectiveness, and robustness of the proposed framework are demonstrated over a large data set, consisting of more than 13 hours of soccer video, captured in different countries and under different conditions. Summarization is an essential requirement for achieving a more compact and interesting representation of sports video contents. We propose a framework that integrates highlights into play segments and reveal why we should still retain breaks. Experimental results show that fast detections of whistle sounds, crowd excitement, and text boxes can complement existing techniques for play-breaks and highlights localization. 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. 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. 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. Tracking players in sports videos presents numerous challenges due to weak distinguishing features and unpredictable motion. Considerable work has been done to track players in such videos using a combination of appearance and motion modeling, mostly in continuous streams of video. However, in a broadcast sports video, having advertisements, replays and intermittent change of camera view, it becomes a challenging task to keep track of players over an entire game. In this work, we solve a novel problem of tracking over a sequence of temporally disjoint soccer videos without the use of appearance cue, using a Graph based optimization approach. Each team is represented by a graph, in which the nodes correspond to player positions and the edge weights depend on spatial inter-player distance. We use team formation to associate tracks between clips and provide an end-to-end system that is able to perform statistical and tactical analysis of the game. We also introduce a new challenging dataset of an international soccer game. This paper proposes the automatic annotation of sports video for content-based retrieval. Conventional methods using position information of objects such as locus, relative positions, their transitions, etc., as indices, have drawbacks that tracking errors of a certain object due to occlusions causes recognition failures, and that representation by position information essentially has a limited number of recognizable events in the retrieval. Our approach incorporates human behavior analysis and specific domain knowledge with conventional methods, to develop an integrated reasoning module for richer expressiveness of events and robust recognition. Based on the proposed method, we implemented a content-based retrieval system which can identify several actions on real tennis video. We select court and net lines, players' positions, ball positions, and players' actions, as indices. Court and net lines are extracted using a court model and Hough transforms. Players and ball positions are tracked by adaptive template matching and particular predictions against sudden changes of motion direction. Players' actions are analyzed by 2D appearance-based matching using the transition of players' silhouettes and a hidden Markov model. The results using two sets of tennis video is presented, demonstrating the performance and the validity of our approach. We present a novel approach for robust localization of multiple people observed using multiple cameras. We use this location information to generate sports visualizations, which include displaying a virtual offside line in soccer games, and showing players' positions and motion patterns. Our main contribution is the modeling and analysis for the problem of fusing corresponding players' positional information as finding minimum weight K-length cycles in complete K-partite graphs. To this end, we use a dynamic programming based approach that varies over a continuum of being maximally to minimally greedy in terms of the number of paths explored at each iteration. We present an end-to-end sports visualization framework that employs our proposed algorithm-class. We demonstrate the robustness of our framework by testing it on 60,000 frames of soccer footage captured over 5 different illumination conditions, play types, and team attire. 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. We present a model that uses a single first-person image to generate an egocentric basketball motion sequence in the form of a 12D camera configuration trajectory, which encodes a player's 3D location and 3D head orientation throughout the sequence. To do this, we first introduce a future convolutional neural network (CNN) that predicts an initial sequence of 12D camera configurations, aiming to capture how real players move during a one-on-one basketball game. We also introduce a goal verifier network, which is trained to verify that a given camera configuration is consistent with the final goals of real one-on-one basketball players. Next, we propose an inverse synthesis procedure to synthesize a refined sequence of 12D camera configurations that (1) sufficiently matches the initial configurations predicted by the future CNN, while (2) maximizing the output of the goal verifier network. Finally, by following the trajectory resulting from the refined camera configuration sequence, we obtain the complete 12D motion sequence. Our model generates realistic basketball motion sequences that capture the goals of real players, outperforming standard deep learning approaches such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and generative adversarial networks (GANs). 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. 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. 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. Recognition of player actions in broadcast sports video is a challenging task due to low resolution of the players in video frames. In this paper, we present a novel method to recognize the basic player actions in broadcast tennis video. Different from the existing appearance-based approaches, our method is based on motion analysis and considers the relationship between the movements of different body parts and the regions in the image plane. A novel motion descriptor is proposed and supervised learning is employed to train the action classifier. We also propose a novel framework by combining the player action recognition with other multimodal features for semantic and tactic analysis of the broadcast tennis video. Incorporating action recognition into the framework not only improves the semantic indexing and retrieval performance of the video content, but also conducts highlights ranking and tactics analysis in tennis matches, which is the first solution to our knowledge for tennis game. The experimental results demonstrate that our player action recognition method outperforms existing appearance-based approaches and the multimodal framework is effective for broadcast tennis video analysis. 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. This paper presents a method to assess a basketball player's performance from his her first-person video. A key challenge lies in the fact that the evaluation metric is highly subjective and specific to a particular evaluator. We leverage the first-person camera to address this challenge. The spatiotemporal visual semantics provided by a first-person view allows us to reason about the camera wearer's actions while he she is participating in an unscripted basketball game. Our method takes a player's first-person video and provides a player's performance measure that is specific to an evaluator's preference. To achieve this goal, we first use a convolutional LSTM network to detect atomic basketball events from first-person videos. Our network's ability to zoom-in to the salient regions addresses the issue of a severe camera wearer's head movement in first-person videos. The detected atomic events are then passed through the Gaussian mixtures to construct a highly non-linear visual spatiotemporal basketball assessment feature. Finally, we use this feature to learn a basketball assessment model from pairs of labeled first-person basketball videos, for which a basketball expert indicates, which of the two players is better. We demonstrate that despite not knowing the basketball evaluator's criterion, our model learns to accurately assess the players in real-world games. Furthermore, our model can also discover basketball events that contribute positively and negatively to a player's performance. Camera network systems generate large volumes of potentially useful data, but extracting value from multiple, related videos can be a daunting task for a human reviewer. Multicamera video summarization seeks to make this task more tractable by generating a reduced set of output summary videos that concisely capture important portions of the input set. We present a system that approaches summarization at the level of detected activity motifs and shortens the input videos by compacting the representation of individual activities. Additionally, redundancy is removed across camera views by omitting from the summary activity occurrences that can be predicted by other occurrences. The system also detects anomalous events within a unified framework and can highlight them in the summary. Our contributions are a method for selecting useful parts of an activity to present to a viewer using activity motifs and a novel framework to score the importance of activity occurrences and allow transfer of importance between temporally related activities without solving the correspondence problem. We provide summarization results for a two camera network, an eleven camera network, and data from PETS 2001. We also include results from Amazon Mechanical Turk human experiments to evaluate how our visualization decisions affect task performance. This paper addresses the problem of automatic player identification in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this setting is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires sophisticated approaches to capture distinctive features from players to distinguish them. To this end, we use Convolutional Neural Networks (CNN) features extracted at multiple scales and encode them with an advanced pooling, called Fisher vector. We leverage it for exploring representations that have sufficient discriminatory power and ability to magnify subtle differences. We also analyze the distinguishing parts of the players and present a part based pooling approach to use these distinctive feature points. The resulting player representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96 on NBA basketball clips. Filming team sports is challenging because there are many points of interest which are constantly changing. Unlike previous automatic broadcasting solutions, we propose a data-driven approach for determining where a robotic pan-tilt-zoom (PTZ) camera should look. Without using any pre-defined heuristics, we learn the relationship between player locations and corresponding camera configurations by crafting features which can be derived from noisy player tracking data, and employ a new calibration algorithm to estimate the pan-tilt-zoom configuration of a human operated broadcast camera at each video frame. Using this data, we train a regress or to predict the appropriate pan angle for new noisy input tracking data. We demonstrate our system on a high school basketball game. Our experiments show how our data-driven planning approach achieves superior performance to a state-of-the-art algorithm and does indeed mimic a human operator. 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.