Abstract
A key question in machine perception is how to adaptively build upon existing capabilities so as to permit novel functionalities. Implicit in this are the notions of anomaly detection and learning transfer. A perceptual system must firstly determine at what point the existing learned model ceases to apply, and secondly, what aspects of the existing model can be brought to bear on the newly-defined learning domain. Anomalies must thus be distinguished from mere outliers, i.e. cases in which the learned model has failed to produce a clear response; it is also necessary to distinguish novel (but meaningful) input from misclassification error within the existing models. We thus apply a methodology of anomaly detection based on comparing the outputs of strong and weak classifiers [10] to the problem of detecting the rule-incongruence involved in the transition from singles to doubles tennis videos. We then demonstrate how the detected anomalies can be used to transfer learning from one (initially known) rule-governed structure to another. Our ultimate aim, building on existing annotation technology, is to construct an adaptive system for court-based sport video annotation.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Almajai, I., Kittler, J., de Campos, T., Christmas, W., Yan, F., Windridge, D., Khan, A.: Ball event recognition using HMM for automatic tennis annotation. In: Proc. Int. Conf. on Image Processing, Hong Kong, September 26-29 (2010) (in Press)
Burget, L., Schwarz, P., Matejka, P., Hannemann, M., Rastrow, A., White, C., Khudanpur, S., Hermansky, H., Cernock, J.: Combination of strongly and weakly constrained recognizers for reliable detection of OOVs. In: Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4081–4084 (2008)
de Campos, T., Barnard, M., Mikolajczyk, K., Kittler, J., Yan, F., Christmas, W., Windridge, D.: An evaluation of bags-of-words and spatio-temporal shapes for action recognition. In: Proc. of the 10th IEEE Workshop on Applications of Computer Vision, Kona, Hawaii, January 5-6 (2011)
Khan, A., Windridge, D., de Campos, T., Kittler, J., Christmas, W.: Lattice-based anomaly rectification for sport video annotation. In: Proc. ICPR (2010)
Kittler, J., Christmas, W.J., Yan, F., Kolonias, I., Windridge, D.: A memory architecture and contextual reasoning for cognitive vision. In: Proc. SCIA, pp. 343–358 (2005)
Kläser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: 19th British Machine Vision Conference, pp. 995–1004 (2008)
Kolonias, I.: Cognitive Vision Systems for Video Understanding and Retrieval. PhD thesis, University of Surrey (2007)
Tommasi, T., Caputo, B.: The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories. In: British Machine Vision Conference (2009)
Wang, H., Ullah, M.M., Käser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: 20th British Machine Vision Conference (2009)
Weinshall, D., Hermansky, H., Zweig, A., Luo, J., Jimison, H., Ohl, F., Pavel, M.: Beyond novelty detection: Incongruent events, when general and specific classifiers disagree. In: Advances in Neural Information Processing Systems (NIPS) (December 2009)
Yan, F., Christmas, W., Kittler, J.: Layered data association using graph-theoretic formulation with application to tennis ball tracking in monocular sequences. Transactions on Pattern Analysis and Machine Intelligence (2008)
Young, S., Kershaw, D., Odell, J., Ollason, D., Valtchev, V., Woodland, P.: The HTK Book Version 3.0. Cambridge University Press, Cambridge (2000)
Zimmermann, K., Svoboda, T., Matas, J.: Adaptive parameter optimization for real-time tracking. In: Proc. ICCV, Workshop on Non-rigid Registration and Tracking through Learning (2007)
Zimmermann, K., Svoboda, T., Matas, J.: Simultaneous learning of motion and appearance. In: The 1st International Workshop on Machine Learning for Vision-based Motion Analysis, Marseilles (2008), In Conjunction with ECCV
Zweig, A., Weinshall, D.: Exploiting object hierarchy: Combining models from different category levels. In: IEEE 11th International Conference on Computer Vision (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Almajai, I. et al. (2012). Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation. In: Weinshall, D., Anemüller, J., van Gool, L. (eds) Detection and Identification of Rare Audiovisual Cues. Studies in Computational Intelligence, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24034-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-24034-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24033-1
Online ISBN: 978-3-642-24034-8
eBook Packages: EngineeringEngineering (R0)