499 research outputs found
Automatic Summarization of Soccer Highlights Using Audio-visual Descriptors
Automatic summarization generation of sports video content has been object of
great interest for many years. Although semantic descriptions techniques have
been proposed, many of the approaches still rely on low-level video descriptors
that render quite limited results due to the complexity of the problem and to
the low capability of the descriptors to represent semantic content. In this
paper, a new approach for automatic highlights summarization generation of
soccer videos using audio-visual descriptors is presented. The approach is
based on the segmentation of the video sequence into shots that will be further
analyzed to determine its relevance and interest. Of special interest in the
approach is the use of the audio information that provides additional
robustness to the overall performance of the summarization system. For every
video shot a set of low and mid level audio-visual descriptors are computed and
lately adequately combined in order to obtain different relevance measures
based on empirical knowledge rules. The final summary is generated by selecting
those shots with highest interest according to the specifications of the user
and the results of relevance measures. A variety of results are presented with
real soccer video sequences that prove the validity of the approach
Shot Classification in Broadcast Soccer Video
In this paper, we present an effective hierarchical shot classification scheme for broadcast soccer video. We first partition a video into replay and non-replay shots with replay logo detection. Then, non-replay shots are further classified into Long, Medium, Close-up or Out-field types with color and texture features based on a decision tree. We tested the method on real broadcast FIFA soccer videos, and the experimental results demonstrate its effectiveness.
Audiovisual framework for automatic soccer highlights generation
Extracting low-level and mid-level descriptors from a soccer match to generate a summary of soccer highlights.Automatic generation of sports highlights from recorded audiovisual content has been object of great interest in recent years. The problem is indeed especially important in the production of second and third division highlights videos where the quantity of raw material is significant and does not contain manual annotations. In this thesis, a new approach for automatic generation of soccer highlights is proposed. The approach is based on the segmentation of the video sequence into shots that will be further ana- lyzed to determine its relevance and interest. For every video shot a set of low and mid level audio-visual descriptors are computed and combined in order to obtain different relevance measures based on empirical knowledge rules. The final summary is generated by selecting those shots with highest interest according to the specifications of the user and the results of relevance measures. The main novelties of this work have been the temporal combination of two shot boundary detectors; the selection of keyframes using motion and color features; the generation of new soccer audio mid-level descriptors; the robust detection of soccer players; the employment of a novel object detection technique to spot goal-posts and finally, the creation of a flexible and user-friendly highlight gen- eration framework. The thesis is mainly devoted to the description of the global visual segmentation module, the selection of audiovisual descriptors and the general scheme for evaluating the measures of relevance. Several results have been produced using real soccer video sequences that prove the validity of the proposed framework
Integrated analysis of audiovisual signals and external information sources for event detection in team sports video
Ph.DDOCTOR OF PHILOSOPH
Data-Driven Analytics for Decision Making in Game Sports
Performance analysis and good decision making in sports is important to maximize chances of winning. Over the last years the amount and quality of data which is available for the analysis has increased enormously due to technical developments like, e.g., of sensor technologies or computer vision technology. However, the data-driven analysis of athletes and team performances is very demanding. One reason is the so called semantic gap of sports analytics. This means that the concepts of coaches are seldomly represented in the data for the analysis. Furthermore, sports in general and game sports in particular present a huge challenge due to its dynamic characteristics and the multi-factorial influences on an athlete’s performance like, e.g., the numerous interaction processes during a match. This requires different types of analyses like, e.g., qualitative analyses and thus anecdotal descriptions of performances up to quantitative analyses with which performances can be described through statistics and indicators. Additionally, coaches and analysts have to work under an enormous time pressure and decisions have to be made very quickly.
In order to facilitate the demanding task of game sports analysts and coaches we present a generic approach how to conceptualize and design a Data Analytics System (DAS) for an efficient support of the decision making processes in practice. We first introduce a theoretical model and present a way how to bridge the semantic gap of sports analytics. This ensures that DASs will provide relevant information for the decision makers. Moreover, we show that DASs need to combine qualitative and quantitative analyses as well as visualizations. Additionally, we introduce different query types which are required for a holistic retrieval of sports data. We furthermore show a model for the user-centered planning and designing of the User Experience (UX) of a DAS.
Having introduced the theoretical basis we present SportSense, a DAS to support decision making in game sports. Its generic architecture allows a fast adaptation to the individual characteristics and requirements of different game sports. SportSense is novel with respect to the fact that it unites raw data, event data, and video data. Furthermore, it supports different query types including an intuitive sketch-based retrieval and seamlessly combines qualitative and quantitative analyses as well as several data visualization options. Moreover, we present the two applications SportSense Football and SportSense Ice Hockey which contain sport-specific concepts and cover (high-level) tactical analyses
Take your Eyes off the Ball: Improving Ball-Tracking by Focusing on Team Play
Accurate video-based ball tracking in team sports is important for automated game analysis, and has proven very difficult because the ball is often occluded by the players. In this paper, we propose a novel approach to addressing this issue by formulating the tracking in terms of deciding which player, if any, is in possession of the ball at any given time. This is very different from standard approaches that first attempt to track the ball and only then to assign possession. We will show that our method substantially increases performance when applied to long basketball and soccer sequences
- …