On the automated interpretation and indexing of American football

This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances.

Using camera motion to identify different types of American football plays

This paper presents a method that uses camera motion parameters to recognise 7 types of American football plays. The approach is based on the motion information extracted from the video and it can identify short and long pass plays, short and long running plays, quarterback sacks, punt plays and kickoff plays. This method has the advantage that it is fast and it does not require player or ball tracking. The system was trained and tested using 782 plays and the results show that the system has an overall classification accuracy of 68%.

Combining NL processing and video data to query American football

We explore the use of natural language understanding and image processing to index and query American Football tapes. We present a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain, and a recognition system which uses the model to recognise American Football plays.

The population cost-effectiveness of interventions designed to prevent childhood depression

BACKGROUND AND OBJECTIVES: Depression in childhood and adolescence is common and often persists into adulthood. This study assessed the population-level cost-effectiveness of a preventive intervention that screens children and adolescents for symptoms of depression in schools and the subsequent provision of a psychological intervention to those showing elevated signs of depression. The target population for screening comprised 11- to 17-year-old children and adolescents in the 2003 Australian population.

METHODS: Economic modeling techniques were used to assess the incremental cost-effectiveness of the intervention compared with no intervention. The perspective was that of the health sector, and outcomes were measured by using disability-adjusted life-years (DALYs). Multivariate probabilistic and univariate sensitivity testing was applied to quantify variations in the model parameters.

RESULTS: The modeled psychological intervention had an incremental cost-effectiveness ratio of $5400 per DALY averted, with just 2% of iterations falling above a $50 000 per DALY value-for-money threshold. Results were robust to model assumptions.

CONCLUSIONS: After school screening, screening and the psychological intervention represent good value-for-money. Such an intervention needs to be seriously considered in any national package of preventive health services. Acceptability issues, particularly to intervention providers, including schools and mental health professionals, need to be considered before wide-scale adoption.