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 predictive capability. In this paper, we propose an “augmented- Hidden Conditional Random Field” (a-HCRF) which incorporates the local observation within the HCRF which boosts its forecasting performance. Given an enormous amount of tracking data from vision-based systems, we show that our approach outperforms current state-of-the- art 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.
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