1,446 research outputs found

    Pervasive decision support to predict football corners and goals by means of data mining

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    Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.Future work will pass for adding new variables to these models, to try different scenar ios in order to obtain models with even greater precision to be added later to the prototype. In parallel, the prototype will be converted in to a system able to disseminate all the probabilities anywhere and anytime in mobile or situated devices. This prot otype also will incorporate the other predictions made in this field related to the final result [22, 23, 24]. Acknowledgments This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/201

    Soccer Coach Decision Support System

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    The savage essence and nature of sports means those who work on it hunt for the win. The sport enterprise is undergoing a gigantic digital transformation focused on imaging, real time and data analysis employed in the competitions. Conventional process methods in sports management such as fitness and health establishments, training, growth and match or game realisation are all being revolutionized by the sport digitization. In team sports it is well known that is needful an enough and simple digital methodology to organize and construct a feasible strategy. Digitization in sports is perpetually evolving and requires pervasive challenges. The sports and athletics digitization success is based on what is being done with collection of more data. Competitive advantages go to those who produce powerful operations using the data and acting on it in real time. The potential impact of these sport features in sport team operations is powerful. Data does not ride all decisions, but it empowers knowledgeable decisions. In these world circumstances, our vision with this system was born from a dream helping soccer sport management systems embrace and improve its contest success. Our perspective problem is how a decision support system for soccer coaches helps them to take enhancement decisions better. To face this problem we have created a soccer coach decision support system. This system is organised in two joined components; the first simulates the prediction of the soccer match winner through a data driven neural network. This component output activates the second to operate the logic rules learning and provides the stats, analysis, decision making and additionally plans improvements like drills and training procedures. This helps on the preparation towards upcoming matches as well as being aligned with their style and playing concepts. Future scalability and development, will analyse the mental and moral features of the teams by virtue of their athlete’s behavior changes

    The collection, analysis and exploitation of footballer attributes: A systematic review

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    © 2022 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Non Commercial License (CC BY-NC 4.0)There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.Peer reviewedFinal Published versio

    Communities in temporal networks: from theoretical underpinnings to real-life applications

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    Static aggregations of network activity can unravel attributes of the complex systems they represent. However, they fall short when the structure of the systems changes over time. In some cases, changes are sluggish, such as in power grids, where lines enjoy a lengthy temporal permanence. In others, a high frequency of change is observed, such as on a network of online messages, social contacts, pathogen transmission or ball passing in a soccer game. In these cases, reducing what is inherently a temporal network to a static one, leads necessarily to a loss of information, such as causal relationships, precedence or reachability rules. Temporal networks are thus the main subject of this thesis, centered on the study of network evolution from the point of view of its clusters as significant meso-structures. The thesis has two interrelated parts. In the first, theoretical challenges are addressed and original algorithms, methods and tools are developed that can further the study of network theory. In the second, these developments are applied to the analysis of team invasion sports. A measurement of game dynamics was created based on a temporal network representation of a match, with nodes clustered by spatial proximity. These measurements were found to correlate with match events of known dynamics. Moreover, they reveal unique, multi-level, aspects of the game, from the individual players contributions, to the clusters of interacting players, to their teams and their matches, which is useful for game analysis, training and strategy development.As agregações estáticas das ligações de uma rede podem revelar atributos dos sistemas complexos que representam. Todavia, são insuficientes quando a estrutura dos sistemas se altera com o tempo. Em alguns casos, as transformações são lentas, tais como em redes de transmissão de eletricidade em que as linhas se mantêm inalteráveis por largos períodos de tempo. Noutras, regista-se uma taxa elevada de mudança, como por exemplo numa rede de mensagens em linha, contatos sociais, transmissão de patógenos ou passes num jogo de futebol. Nestes casos, reduzir o que é inerentemente uma rede temporal a uma rede estática, leva a uma perda de informação, tais como relações causais, regras de precedência ou de acessibilidade. Redes temporais são assim o tema desta tese, centrada nos seus agrupamentos, como meso-estruturas significantes. A tese está dividida em duas partes. Na primeira, são considerados problemas teóricos, e são desenvolvidos algoritmos, métodos e ferramentas que avançam o estudo da teoria de redes. Na segunda, estes desenvolvimentos são aplicados à análise de jogos desportivos coletivos de invasão. Foi criada uma medida de dinâmica do jogo, baseada na representação da partida através de uma rede temporal de nós agrupados por proximidade espacial. Os resultados obtidos correlacionam-se com eventos do jogo de dinâmica conhecida. Adicionalmente, esta medida revela aspetos únicos e multi-nível da dinâmica do jogo, desde a contribuição individual do jogador, até aos agrupamentos de jogadores, da equipa e das partidas, útil para a análise do jogo, de treino e de desenvolvimento estratégico

    Audio-visual football video analysis, from structure detection to attention analysis

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    Sport video is an important video genre. Content-based sports video analysis attracts great interest from both industry and academic fields. A sports video is characterised by repetitive temporal structures, relatively plain contents, and strong spatio-temporal variations, such as quick camera switches and swift local motions. It is necessary to develop specific techniques for content-based sports video analysis to utilise these characteristics. For an efficient and effective sports video analysis system, there are three fundamental questions: (1) what are key stories for sports videos; (2) what incurs viewer’s interest; and (3) how to identify game highlights. This thesis is developed around these questions. We approached these questions from two different perspectives and in turn three research contributions are presented, namely, replay detection, attack temporal structure decomposition, and attention-based highlight identification. Replay segments convey the most important contents in sports videos. It is an efficient approach to collect game highlights by detecting replay segments. However, replay is an artefact of editing, which improves with advances in video editing tools. The composition of replay is complex, which includes logo transitions, slow motions, viewpoint switches and normal speed video clips. Since logo transition clips are pervasive in game collections of FIFA World Cup 2002, FIFA World Cup 2006 and UEFA Championship 2006, we take logo transition detection as an effective replacement of replay detection. A two-pass system was developed, including a five-layer adaboost classifier and a logo template matching throughout an entire video. The five-layer adaboost utilises shot duration, average game pitch ratio, average motion, sequential colour histogram and shot frequency between two neighbouring logo transitions, to filter out logo transition candidates. Subsequently, a logo template is constructed and employed to find all transition logo sequences. The precision and recall of this system in replay detection is 100% in a five-game evaluation collection. An attack structure is a team competition for a score. Hence, this structure is a conceptually fundamental unit of a football video as well as other sports videos. We review the literature of content-based temporal structures, such as play-break structure, and develop a three-step system for automatic attack structure decomposition. Four content-based shot classes, namely, play, focus, replay and break were identified by low level visual features. A four-state hidden Markov model was trained to simulate transition processes among these shot classes. Since attack structures are the longest repetitive temporal unit in a sports video, a suffix tree is proposed to find the longest repetitive substring in the label sequence of shot class transitions. These occurrences of this substring are regarded as a kernel of an attack hidden Markov process. Therefore, the decomposition of attack structure becomes a boundary likelihood comparison between two Markov chains. Highlights are what attract notice. Attention is a psychological measurement of “notice ”. A brief survey of attention psychological background, attention estimation from vision and auditory, and multiple modality attention fusion is presented. We propose two attention models for sports video analysis, namely, the role-based attention model and the multiresolution autoregressive framework. The role-based attention model is based on the perception structure during watching video. This model removes reflection bias among modality salient signals and combines these signals by reflectors. The multiresolution autoregressive framework (MAR) treats salient signals as a group of smooth random processes, which follow a similar trend but are filled with noise. This framework tries to estimate a noise-less signal from these coarse noisy observations by a multiple resolution analysis. Related algorithms are developed, such as event segmentation on a MAR tree and real time event detection. The experiment shows that these attention-based approach can find goal events at a high precision. Moreover, results of MAR-based highlight detection on the final game of FIFA 2002 and 2006 are highly similar to professionally labelled highlights by BBC and FIFA

    A Fuzzy Logic-Based System for Soccer Video Scenes Classification

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    Massive global video surveillance worldwide captures data but lacks detailed activity information to flag events of interest, while the human burden of monitoring video footage is untenable. Artificial intelligence (AI) can be applied to raw video footage to identify and extract required information and summarize it in linguistic formats. Video summarization automation usually involves text-based data such as subtitles, segmenting text and semantics, with little attention to video summarization in the processing of video footage only. Classification problems in recorded videos are often very complex and uncertain due to the dynamic nature of the video sequence and light conditions, background, camera angle, occlusions, indistinguishable scene features, etc. Video scene classification forms the basis of linguistic video summarization, an open research problem with major commercial importance. Soccer video scenes present added challenges due to specific objects and events with similar features (e.g. “people” include audiences, coaches, and players), as well as being constituted from a series of quickly changing and dynamic frames with small inter-frame variations. There is an added difficulty associated with the need to have light weight video classification systems working in real time with massive data sizes. In this thesis, we introduce a novel system based on Interval Type-2 Fuzzy Logic Classification Systems (IT2FLCS) whose parameters are optimized by the Big Bang–Big Crunch (BB-BC) algorithm, which allows for the automatic scenes classification using optimized rules in broadcasted soccer matches video. The type-2 fuzzy logic systems would be unequivocal to present a highly interpretable and transparent model which is very suitable for the handling the encountered uncertainties in video footages and converting the accumulated data to linguistic formats which can be easily stored and analysed. Meanwhile the traditional black box techniques, such as support vector machines (SVMs) and neural networks, do not provide models which could be easily analysed and understood by human users. The BB-BC optimization is a heuristic, population-based evolutionary approach which is characterized by the ease of implementation, fast convergence and low computational cost. We employed the BB-BC to optimize our system parameters of fuzzy logic membership functions and fuzzy rules. Using the BB-BC we are able to balance the system transparency (through generating a small rule set) together with increasing the accuracy of scene classification. Thus, the proposed fuzzy-based system allows achieving relatively high classification accuracy with a small number of rules thus increasing the system interpretability and allowing its real-time processing. The type-2 Fuzzy Logic Classification System (T2FLCS) obtained 87.57% prediction accuracy in the scene classification of our testing group data which is better than the type-1 fuzzy classification system and neural networks counterparts. The BB-BC optimization algorithms decrease the size of rule bases both in T1FLCS and T2FLCS; the T2FLCS finally got 85.716% with reduce rules, outperforming the T1FLCS and neural network counterparts, especially in the “out-of-range data” which validates the T2FLCSs capability to handle the high level of faced uncertainties. We also presented a novel approach based on the scenes classification system combined with the dynamic time warping algorithm to implement the video events detection for real world processing. The proposed system could run on recorded or live video clips and output a label to describe the event in order to provide the high level summarization of the videos to the user

    Annual Report of Undergraduate Research Fellows, August 2009 to May 2010

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    Annual Report of Undergraduate Research Fellows from August 2009 to May 2010
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