Abstract
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 it 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Each side of the tennis court is 11 m wide and 11.9 m long.
References
Kim, K., Grundmann, M., Shamir, A., Matthews, I., Hodgins, J., Essa, I.: Motion fields to predict play evolution in dynamic sport scenes. In: CVPR (2010)
Wei, X., Lucey, P., Morgan, S., Sridharan, S.: Sweet-spot: using spatiotemporal data to discover and predict shots in tennis. In: MIT Sloan Sports Analytics Conference (2013)
Wei, X., Lucey, P., Morgan, S., Sridharan, S.: Predicting shot locations in tennis using spatiotemporal data. In: DICTA (2013)
Carr, P., Mistry, M., Matthews, I.: Hybrid robotic/virtual pan-tilt-zoom cameras for autonomous event recording. In: ACM Multimedia (2013)
Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Matthews, I.: “Win at home and draw away”: automatic formation analysis highlighting the differences in home and away team behaviors. In: MIT Sloan Sports Analytics Conference (2014)
Lucey, P., Bialkowski, A., Carr, P., Yue, Y., Matthews, I.: “How to get an open shot”: analyzing team movement in basketball using tracking data. In: MIT Sloan Sports Analytics Conference (2014)
Quattoni, A., Wang, L., Morency, L., Collins, M., Darrell, T.: Hidden-state conditional random fields. PAMI 29, 1848–1852 (2007)
Ryoo, M.S.: Human activity prediction: early recognition of ongoing activities from streaming videos. In: ICCV (2011)
Hoai, M., Torre, F.: Max-margin early event detectors. In: CVPR (2012)
Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: CVPR (2009)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using a social force model. In: CVPR (2009)
Kitani, K.M., Ziebart, B.D., Bagnell, J.A., Hebert, M.: Activity forecasting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 201–214. Springer, Heidelberg (2012)
Zhou, B., Wang, X., Tang, X.: Understanding collective crowd behaviors: learning a mixture model of dynamic pedestrian-agents. In: CVPR (2012)
Kim, K., Lee, D., Essa, I.: Detecting regions of interest in dynamic scenes with camera motions. In: CVPR (2012)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML (2001)
Wang, S., Quattoni, A., Morency, L., Demirdjian, D.: Hidden conditional random fields for gesture recognition. In: CVPR (2006)
Wang, Y., Mori, G.: Max-margin hidden conditional random fields for human action recognition. In: CVPR (2009)
Batra, D., Yadollahpour, P., Guzman-Rivera, A., Shakhnarovich, G.: Diverse M-best solutions in markov random fields. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 1–16. Springer, Heidelberg (2012)
Wang, X., Ablavsky, V., Shitrit, H.B., Fua, P.: Take your eyes off the ball: improving ball-tracking by focusing on team play. In: CVIU (2013)
SportsVision. http://www.sportsvision.com.au
Stats. http://www.stats.com/
Hawk-Eye. http://www.hawkeyeinnovations.co.uk
Lucey, P., Bialkowski, A., Carr, P., Foote, E., Matthews, I.: Characterizing multi-agent team behavior from partial team tracings: evidence from the English premier league. In: AAAI (2012)
Lucey, P., Oliver, D., Carr, P., Roth, J., Matthews, I.: Assessing team strategy using spatiotemporal data. In: ACM SIGKDD Conference on Knowledge, Discovery and Data Mining (KDD) (2013)
Wei, X., Sha, L., Lucey, P., Morgan, S., Sridharan, S.: Large-scale analysis of formations in soccer. In: DICTA (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wei, X., Lucey, P., Vidas, S., Morgan, S., Sridharan, S. (2015). Forecasting Events Using an Augmented Hidden Conditional Random Field. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-16817-3_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16816-6
Online ISBN: 978-3-319-16817-3
eBook Packages: Computer ScienceComputer Science (R0)