Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation
Almajai, I, Yan, F, de Campos, T, Khan, A, Christmas, W, Windridge, D and Kittler, J (2012) Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation In: DIRAC Workshop on Detection and Identification of Rare Audivisual Cues, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010-09-24 - 2010-09-24, Barcelona, Spain.
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Available under License : See the attached licence file.
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 newlydefined 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  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.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Identification Number :||10.1007/978-3-642-24034-8_9|
|Additional Information :||The original publication is available at http://www.springerlink.com|
|Depositing User :||Symplectic Elements|
|Date Deposited :||25 Mar 2015 10:48|
|Last Modified :||25 Mar 2015 14:33|
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