Sports events and the female market : an investigation of attendance motivations

Over the last five years, attendance at sports events in Australia has grown at a rate of 13%; however, male spectators outnumber female spectators by 25% (ABS 2003). Drawing on a sample of 175 female respondents from the city and suburbs of Melbourne, this study identifies and explores the factors that motivate their attendance at sports events. The results show that social dimensions as well as on site entertainment can have a strong influence in attracting a female audience and that omen will
not attend if the facilities provided are not of a high standard.

A statistical-driven approach for automatic classification of events in AFL video highlights

Due to the repetitive and lengthy nature, automatic content-based summarization is essential to extract a more compact and interesting representation of sport video. State-of-the art approaches have confirmed that high-level semantic in sport video can be detected based on the occurrences of specific audio and visual features (also known as cinematic). However, most of them still rely heavily on manual investigation to construct the algorithms for highlight detection. Thus, the primary aim of this paper is to demonstrate how the statistics of cinematic features within play-break sequences can be used to less-subjectively construct highlight classification rules. To verify the effectiveness of our algorithms, we will present some experimental results using six AFL (Australian Football League) matches from different broadcasters. At this stage, we have successfully classified each play-break sequence into: goal, behind, mark, tackle, and non-highlight. These events are chosen since they are commonly used for broadcasted AFL highlights. The proposed algorithms have also been tested successfully with soccer video.

Towards universal and statistical-driven heuristics for automatic classification of sports video events

Researchers worldwide have been actively seeking for the most robust and powerful solutions to detect and classify key events (or highlights) in various sports domains. Most approaches have employed manual heuristics that model the typical pattern of audio-visual features within particular sport events To avoid manual observation and knowledge, machine-learning can be used as an alternative approach. To bridge the gaps between these two alternatives, an attempt is made to integrate statistics into heuristic models during highlight detection in our investigation. The models can be designed with a modest amount of domain-knowledge, making them less subjective and more robust for different sports. We have also successfully used a universal scope of detection and a standard set of features that can be applied for different sports that include soccer, basketball and Australian football. An experiment on a large dataset of sport videos, with a total of around 15 hours, has demonstrated the effectiveness and robustness of our
aIlgorithms.

Extensible detection and indexing of highlight events in broadcasted sports video

Content-based indexing is fundamental to support and sustain the ongoing growth of broadcasted sports video. The main challenge is to design extensible frameworks to detect and index highlight events. This paper presents: 1) A statistical-driven event detection approach that utilizes a minimum amount of manual knowledge and is based on a universal scope-of-detection and audio-visual features; 2) A semi-schema-based indexing that combines the benefits of schema-based modeling to ensure that the video indexes are valid at all time without manual checking, and schema-less modeling to allow several passes of instantiation in which additional elements can be declared. To demonstrate the performance of the events detection, a large dataset of sport videos with a total of around 15 hours including soccer, basketball and Australian football is used.

Using decision-tree to automatically construct learned-heuristics for events classification in sports video

Automatic events classification is an essential requirement for constructing an effective sports video summary. It has become a well-known theory that the high-level semantics in sport video can be “computationally interpreted” based on the occurrences of specific audio and visual features which can be extracted automatically. State-of-the-art solutions for features-based event classification have only relied on either manual-knowledge based heuristics or machine learning. To bridge the gaps, we have successfully combined the two approaches by using learning-based heuristics. The heuristics are constructed automatically using decision tree while manual supervision is only required to check the features and highlight contained in each training segment. Thus, fully automated construction of classification system for sports video events has been achieved. A comprehensive experiment on 10 hours video dataset, with five full-match soccer and five full-match basketball videos, has demonstrated the effectiveness/robustness of our algorithms.

Managing sport facilities and major events

A comprehensive introduction to sports events and facility management, this book guides students through the on-the-job issues and technical problems that sports managers have to address every day.

Special events in the nonprofit sector

Learning Objectives
On completion of this chapter; thereader will
• Define special events and understand the role of special events in the
nonprofit sector
• Describe the different types of nonprofit events
• Describe the factors that influence the objectives of special events
• Understand the issues involved in producing special events
• Understand the risks associated with hosting special events
• Describe the procedures involved in managing special events
• Understand the importance of marketing and public relations for special events in the nonprofit sector

Patient safety, ethics and whistleblowing : a nursing response to the events at the Campbelltown and Camden Hospitals

In November 2002, in what stands as one of the most significant whistleblowing cases in the history of the Australian health care system, four nurses went public with concerns they had about the management of clinical incidents and patient safety at two hospitals in Sydney, New South Wales. The handling of this case and its aftermath raises important moral questions concerning the nature of whistleblowing in health care domains and the possible implications for the patient safety and quality of care movement in Australia. This paper presents an overview of the case, the moral risks associated with whistleblowing, and some lessons learned.