(PDF) Soccer Event Detection via Collaborative Multimodal Feature Analysis and Candidate Ranking IAJIT First Online Publication
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Soccer Event Detection via Collaborative Multimodal Feature Analysis and Candidate Ranking IAJIT First Online Publication
Mohammad Abbasnejad
2011
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Abstract
Abstract: This paper presents a framework for soccer event detection through collaborative analysis of the textual, visual and aural modalities. The basic notion is to decompose a match video into smaller segments until ultimately the desired eventful segment is identified. Simple features are considered namely the minute-by-minute reports from sports websites (i.e. text), the semantic shot classes of far and closeup-views (i.e. visual), and the low-level features of pitch and log-energy (i.e. audio). The framework demonstrates that despite considering simple features, and by averting the use of labeled training examples, event detection can be achieved at very high accuracy. Experiments conducted on ~30-hours of soccer video show very promising results for the detection of goals, penalties, yellow cards and red cards.
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Soccer event detection via collaborative multimodal feature analysis and candidate
ranking
ABSTRACT
This paper presents a framework for soccer event detection through collaborative analysis of
the textual, visual and aural modalities. The basic notion is to decompose a match video into
smaller segments until ultimately the desired eventful segment is identified. Simple features
are considered namely the minute-by-minute reports from sports websites (i.e. text), the
semantic shot classes of far and closeup-views (i.e. visual), and the low-level features of pitch
and log-energy (i.e. audio). The framework demonstrates that despite considering simple
features, and by averting the use of labeled training examples, event detection can be
achieved at very high accuracy. Experiments conducted on ~30-hours of soccer video show
very promising results for the detection of goals, penalties, yellow cards and red cards.
Keyword: Soccer event detection; Sports video analysis; Semantic gap; Webcasting text
April 21, 2022
Mohammad Abbasnejad
Shahid Bahonar University of Kerman, Faculty Member
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Soccer Event Detection via Collaborative Multimodal Feature Analysis and Candidate Ranking
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SOCCER VIDEO EVENT DETECTION VIA COLLABORATIVE TEXTUAL, AURAL AND VISUAL ANALYSIS
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Soccer event detection deals with identifying interesting segments in soccer video via audio/visual content analysis. This task enables automatic high-level index creation, which circumvents large-scale manual annotation and facilitates semantic-based retrieval. This thesis proposes two frameworks for event detection through collaborative analysis of textual, aural and visual features. The frameworks share a common initial component where both utilize an external textual resource, which is the minute-by-minute (MBM) reports from sports broadcasters, to accurately localize sections of video containing the desired events. The first framework identifies an initial estimate of an eventful segment via audio energy analysis. Visual semantic features are then observed to further refine the detected eventful segment. The second framework implements a ranking procedure where semantic visual features are firstly analyzed to generate a shortlist of candidates. This is followed by aural or visual analysis to rank the actual eventful candidate top-most within the respective shortlist. Both frameworks rely on uncomplicated audio/visual feature sets, which is the main advantage compared to previously proposed works. Furthermore, manually labeled data are not needed since audio/visual considerations are based on automatically classified semantic visual features and low-level aural calculations. Evaluation made over a large video dataset shows promising results for goal, penalty, yellow card, red card and substitution events detection.
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Goal Event Detection in Soccer Videos via Collaborative Multimodal Analysis
M. Rajeswari
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Detecting semantic events in sports video is crucial for video indexing and retrieval. Most existing works have exclusively relied on video content features, namely, directly available and extractable data from the visual and/or aural channels. Sole reliance on such data however, can be problematic due to the high-level semantic nature of video and the difficulty to properly align detected events with their exact time of occurrences. This paper proposes a framework for soccer goal event detection through collaborative analysis of multimodal features. Unlike previous approaches, the visual and aural contents are not directly scrutinized. Instead, an external textual source (i.e., minute-by-minute reports from sports websites) is used to initially localize the event search space. This step is vital as the event search space can significantly be reduced. This also makes further visual and aural analysis more efficient since excessive and unnecessary non-eventful segments are discarded, culminating in the accurate identification of the actual goal event segment. Experiments conducted on thirteen soccer matches are very promising with high accuracy rates being reported.
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Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities
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Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.
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SOCCER EVENT DETECTION
Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)
The research community is interested in developing automatic systems for the detection of events in video. This is particularly important in the field of sports data analytics. This paper presents an approach for identifying major complex events in soccer videos, starting from object detection and spatial relations between objects. The proposed framework, firstly, detects objects from each single video frame providing a set of candidate objects with associated confidence scores. The event detection system, then, detects events by means of rules which are based on temporal and logical combinations of the detected objects and their relative distances. The effectiveness of the framework is preliminary demonstrated over different events like "Ball possession" and "Kicking the ball".
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Goal Detection in Soccer Video: Role-Based Events Detection Approach
farhad bayat
International Journal of Electrical and Computer Engineering (IJECE), 2014
Soccer video processing and analysis to find critical events such as occurrences of goal event have been one of the important issues and topics of active researches in recent years. In this paper, a new role-based framework is proposed for goal event detection in which the semantic structure of soccer game is used. Usually after a goal scene, the audiences' and reporters' sound intensity is increased, ball is sent back to the center and the camera may: zoom on Player, show audiences' delighting, repeat the goal scene or display a combination of them. Thus, the occurrence of goal event will be detectable by analysis of sequences of above roles. The proposed framework in this paper consists of four main procedures: 1-detection of game's critical events by using audio channel, 2-detection of shot boundary and shots classification, 3-selection of candidate events according to the type of shot and existence of goalmouth in the shot, 4-detection of restarting the game from the center of the field. A new method for shot classification is also presented in this framework. Finally, by applying the proposed method it was shown that the goal events detection has a good accuracy and the percentage of detection failure is also very low.
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Event Detection Based Approach for Soccer Video Summarization Using Machine learning
Hossam Zawbaa
Many soccer fans prefer to watch a summary of football games as watching a whole soccer match needs a lot of time. Traditionally, soccer videos were analyzed manually, however this costs valuable time. Therefore, it is necessary to have a tool for doing the video analysis and summarization job automatically. Automatic soccer video summarization is about extracting important events from soccer matches in order to produce general summaries for the most important moments in which soccer viewers may be interested. ...
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A novel framework for semantic annotation of soccer sports video sequences
K. Palaniappan
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Manual annotation is both impractical and very expensive, due to the vast amount of data generated at a rapid rate A novel framework is presented for semantic labeling of by such videos. However, automatic annotation is a very video clips, automatically segmented from broadcast video demanding and an extremely challenging computer vision task of soccer (football) games, as highlights and excitement as it involves high-level scene interpretation. In , authors clips etc. The proposed framework provides a generalizable presented a web-casting text based annotation scheme. In method for linking low-level video features with high- [1], authors proposed Finite State Machine based annotation level semantic concepts defined in a commonly understood of soccer video. Barnard et. al. proposed [3] HMM based sports lexicon. Three important contributions are made to framework to fuse audio and video features to recognize the automatic annotation of sports video, as follows. First, domain play and break scenes in soccer video sequences. Li et. al. knowledge combined with an event-lexicon and a four-level proposed rule based algorithm using low-level audio/video hierarchical classifier based on low-level video features is features for football video summarization. Babaguchi et. used to label video segments. Second, a priori event mining al. proposed event detection by recognizing the textual is used to establish probabilistic event-associations that are overlays from football video. used to assign a concept-lexicon, such as goals and saves, to There have been many successful works in soccer video each highlight video segment. And, finally, the collection of analysis as mentioned above. But most of these works fail to highlight video clips is summarized using concept-and eventrespond to action-based queries, such as "extract the goal clips lexicons to facilitate highlight browsing, video skimming, out of this soccer sequence", or "extract the saves from this indexing and retrieval. soccer video", "extract goals scored by team-A", "extract all the red card events from the collection of FIFA 2006 world 1 Introduction cup matches", etc. At higher level, user may ask the specific queries such as, "extract the replay segment from the goal clip
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Tracking Objects from Multiple Soccer Videos and Recognizing Events
Yongduek Seo
Citeseer
This paper presents a novel way of recognizing events from a soc-cer match video sequence. After tracking players and the ball, events such as passing, kicking, having, scoring and struggling for the ball can be inferred for the subsequences. In our frame-work, the ...
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A Novel Learning-Based Framework for Detecting Interesting Events in Soccer Videos
Santanu Chaudhury
2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, 2008
We present a novel learning-based framework for detecting interesting events in soccer videos. The input to the system is a raw soccer video. We have learning at three levels -learning to detect interesting low-level features from image and video data using Support Vector Machines (hereafter, SVMs), and a hierarchical Conditional Random Field-(hereafter, CRF-) based methodology to learn the dependencies of mid-level features and their relation with the lowlevel features, and high level decisions ('interesting events') and their relation with the mid-level features: all on the basis of training video data. Descriptors are spatio-temporal in nature -they can be associated with a region in an image or a set of frames. Temporal patterns of descriptors characterise an event. We apply this framework to parse soccer videos into Interesting (a goal or a goal miss) and Non-Interesting videos. We present results of numerous experiments in support of the proposed strategy.
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