Forecasting Events Using an Augmented Hidden Conditional Random Field [chapter]

Xinyu Wei, Patrick Lucey, Stephen Vidas, Stuart Morgan, Sridha Sridharan
2015 Lecture Notes in Computer Science  
In highly dynamic and adversarial domains such as sports, short-term predictions are made by incorporating both local immediate as well global situational information. For forecasting complex events, higher-order models such as Hidden Conditional Random Field (HCRF) have been used to good effect as capture the long-term, high-level semantics of the signal. However, as the prediction is based solely on the hidden layer, fine-grained local information is not incorporated which reduces its
more » ... ve capability. In this paper, we propose an "augmented-Hidden Conditional Random Field" (a-HCRF) which incorporates the local observation within the HCRF which boosts it forecasting performance. Given an enormous amount of tracking data from vision-based systems, we show that our approach outperforms current state-of-theart methods in forecasting short-term events in both soccer and tennis. Additionally, as the tracking data is long-term and continuous, we show our model can be adapted to recent data which improves performance.
doi:10.1007/978-3-319-16817-3_37 fatcat:jdzyr3rijbal7l4xyofl2eoyxi