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

Audio processing for automatic TV sports program highlights detection

In today’s fast paced world, the time available to watch long sports programmes is decreasing, while the number of sports channels is rapidly increasing. Many viewers desire the facility to watch just the highlights of sports events. This paper presents a simple, but effective, method for generating sports video highlights summaries. Our method detects semantically important events in sports programmes by using the Scale Factors in the MPEG audio bitstream to generate an audio amplitude profile of the program. The Scale Factors for the subbands corresponding to the voice bandwidth give a strong indication of the level of commentator and/or spectator excitement. When periods of sustained high audio amplitude have been detected and ranked, the corresponding video shots may be concatenated to produce a summary of the program highlights. Our method uses only the Scale Factor information that is directly accessible from the MPEG bitstream, without any decoding, leading to highly efficient computation. It is also rather more generic than many existing techniques, being particularly suitable for the more popular sports televised in Ireland such as soccer, Gaelic football, hurling, rugby, horse racing and motor racing

Audio and video processing for automatic TV advertisement detection

As a partner in the Centre for Digital Video Processing, the Visual Media Processing Group at Dublin City University conducts research and development in the area of digital video management. The current stage of development is demonstrated on our Web-based digital video system called Físchlár [1,2], which provides for efficient recording, analyzing, browsing and viewing of digitally captured television programmes. In order to make the browsing of programme material more efficient, users have requested the option of automatically deleting advertisement breaks. Our initial work on this task focused on locating ad-breaks by detecting patterns of silent black frames which separate individual advertisements and/or complete ad-breaks in most commercial TV stations. However, not all TV stations use silent, black frames to flag ad-breaks. We therefore decided to attempt to detect advertisements using the rate of shot cuts in the digitised TV signal. This paper describes the implementation and performance of both methods of ad-break detection

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

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

The critical merger distance between two co-rotating quasi-geostrophic vortices

This paper examines the critical merger or strong interaction distance between two equal-potential-vorticity quasi-geostrophic vortices. The interaction between the two vortices depends on five parameters: their volume ratio, their height-to-width aspect ratios and their vertical and horizontal offsets. Due to the size of the parameter space, a direct investigation solving the full quasi-geostrophic equations is impossible. We instead determine the critical merger distance approximately using an asymptotic approach. We associate the merger distance with the margin of stability for a family of equilibrium states having prescribed aspect and volume ratios, and vertical offset. The equilibrium states are obtained using an asymptotic solution method which models vortices by ellipsoids. The margin itself is determined by a linear stability analysis. We focus on the interaction between oblate to moderately prolate vortices, the shapes most commonly found in turbulence. Here, a new unexpected instability is found and discussed for prolate vortices which is manifested by the tilting of vortices toward each other. It implies than tall vortices may merge starting from greater separation distances than previously thought.Publisher PDFPeer reviewe

Classification of sporting activities using smartphone accelerometers

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach

Defining Markets That Involve Multi-Sided Platform Businesses: An Empirical Framework With an Application to Google's Purchase of DoubleClick

A multi-sided platform (MSP) serves as an intermediary for two or more groups of customers who are linked by indirect network effects. Recent research has found that MSPs are significant in many industries and that some standard economic results, such as the Lerner Index, do not apply to them, in material ways, without some significant modification to take linkages between the multiple sides into account. This article extends several key tools used for the analysis of mergers to situations in which one or more of the suppliers are MSPs. It shows that the application of traditional tools to mergers involving MSPs results in biases the direction of which depends on the particular tool being used and other conditions. It also extends these tools to the analysis of the merger of MSPs. The techniques are illustrated with an application to an acquisition by Google in the online advertising industry.

The shape of vortices in quasi-geostrophic turbulence

Partially supported by the UK EPSRC (Grant GR/N11711)The present work discusses the most commonly occurring shape of the coherent vortical structures in rapidly rotating stably stratified turbulence, under the quasi-geostrophic approximation. In decaying turbulence, these vortices-coherent regions of the materially-invariant potential vorticity-dominate the flow evolution, and indeed the flow evolution is governed by their interactions. An analysis of several exceptionally high-resolution simulations of quasi-geostrophic turbulence is performed. The results indicate that the population of vortices exhibits a mean height-to-width aspect ratio less than unity, in fact close to 0.8. This finding is justified here by a simple model, in which vortices are taken to be ellipsoids of uniform potential vorticity. The model focuses on steady ellipsoids within a uniform background strain flow. This background flow approximates the effects of surrounding vortices in a turbulent flow on a given vortex. It is argued that the vortices which are able to withstand the highest levels of strain are those most likely to be found in the actual turbulent flow. Our calculations confirm that the optimal height-to-width aspect ratio is close to 0.8 for a wide range of background straining flows.Publisher PDFPeer reviewe

Stock Price Prediction using Dynamic Neural Networks

This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data, thus providing a mechanism to predict stock price movements much more precisely than many current techniques. Contemporary methods for stock analysis, including fundamental, technical, and regression techniques, are conversed and paralleled with the performance of neural networks. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with Chaos theory using neural networks. This paper will refute the EMH and support Chaos theory. Finally, recommendations for using neural networks in stock price prediction will be presented