Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

Event detection based on generic characteristics of field-sports

In this paper, we propose a generic framework for event detection in broadcast video of multiple different field-sports. Features indicating significant events are selected, and robust detectors built. These features are rooted in generic characteristics common to all genres of field-sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested across multiple genres of field-sports including soccer, rugby, hockey and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

A framework for event detection in field-sports video broadcasts based on SVM generated audio-visual feature model. Case-study: soccer video

In this paper we propose a novel audio-visual feature-based framework, for event detection in field sports broadcast video. The system is evaluated via a case-study involving MPEG encoded soccer video. Specifically, the evidence gathered by various feature detectors is combined by means of a learning algorithm (a support vector machine), which infers the occurrence of an event, based on a model generated during a training phase, utilizing a corpus of 25 hours of content. The system is evaluated using 25 hours of separate test content. Following an evaluation of results obtained, it is shown for this case, that both high precision and recall statistics are achievable

The protection of sports events in the EU: Property, intellectual property, unfair competition and special forms of protection

This article analyses some of the legal tools available to organisers of sporting events under EU law and the law of EU Member States. The focus is on remedies based on property rights and contracts, as well as on intellectual property, unfair competition rules and so called “special” forms of protection. As it is well known, in fact, following the ECJ ruling in Premier League v. QC Leisure, sporting events as such do not qualify as works under EU copyright law. Nevertheless, the article shows that remedies based on both traditional and new forms of property, IP and cognate rights can still offer adequate protection to sports organisers. First, many sports events take place in dedicated venues on which sports organisers can claim exclusive use rights and thereupon develop conditional access agreements (i.e. “house right”). Second, the recording and broadcast of sporting events may give rise to a variety of intellectual property rights, especially in the field of copyright and related rights. Third, unfair competition rules, and in particular misappropriation doctrines, have been invoked to protect sporting activities from unauthorised copying. Fourth, special forms of protection have recently been devised at the national level in order to offer an additional layer of rights protecting sports organisers. The article argues that even in the absence of a dedicated EU harmonised right tailored to sports events, the current legal framework is well equipped to offer protection to the investments that the sport industry is making in this sector. The article also argues that national initiatives in the field have so far proven of little practical relevance and, as a matter of fact, have the potential to clash with the general EU legal framework. There is only one area that escapes this rule: a right to use sporting events data to organise betting activities, or in other words, a right to consent to bets. The article concludes that if such a right is to be recognised, it is not the field of intellectual property, nor even property in general, the most appropriate area of law at which to look

Customized television: Standards compliant advanced digital television

This correspondence describes a European Union supported collaborative project called CustomTV based on the premise that future TV sets will provide all sorts of multimedia information and interactivity, as well as manage all such services according to each user’s or group of user’s preferences/profiles. We have demonstrated the potential of recent standards (MPEG-4 and MPEG-7) to implement such a scenario by building the following services: an advanced EPG, Weather Forecasting, and Stock Exchange/Flight Information

Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

Real-time event classification in field sport videos

The paper presents a novel approach to real-time event detection in sports broadcasts. We present how the same underlying audio-visual feature extraction algorithm based on new global image descriptors is robust across a range of different sports alleviating the need to tailor it to a particular sport. In addition, we propose and evaluate three different classifiers in order to detect events using these features: a feed-forward neural network, an Elman neural network and a decision tree. Each are investigated and evaluated in terms of their usefulness for real-time event classification. We also propose a ground truth dataset together with an annotation technique for performance evaluation of each classifier useful to others interested in this problem

Automatic speech-to-background ratio selection to maintain speech intelligibility in broadcasts using an objective intelligibility metric

While mixing, sound producers and audio professionals empirically set the speech-to-background ratio (SBR) based on rules of thumb and their own perception of sounds. There is no guarantee that the speech content will be intelligible for the general population consuming content over a wide variety of devices, however. In this study, an approach to automatically determine the appropriate SBR for a scene using an objective intelligibility metric is introduced. The model-estimated SBR needed for a preset minimum intelligibility level was compared to the listener-preferred SBR for a range of background sounds. It was found that an extra gain added to the model estimation is needed even for listeners with normal hearing. This gain is needed so an audio scene can be auditioned with comfort and without compromising the sound effects contributed by the background. When the background introduces little informational masking, the extra gain holds almost constant across the various background sounds. However, a larger gain is required for a background that induces informational masking, such as competing speech. The results from a final subjective rating study show that the model-estimated SBR with the additional gain, yields the same listening experience as the SBR preferred by listeners

Real-time event detection in field sport videos

This chapter describes a real-time system for event detection in sports broadcasts. The approach presented is applicable to a wide range of field sports. Using two independent event detection approaches that work simultaneously, the system is capable of accurately detecting scores, near misses, and other exciting parts of a game that do not result in a score. The results obtained across a diverse dataset of different field sports are promising, demonstrating over 90% accuracy for a feature-based event detector and 100% accuracy for a scoreboard-based detector detecting only score