Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation
Ibrahim Almajai, Fei Yan, Teo de Campos, Aftab Khan, William Christmas, David Windridge and Josef Kittler
In: ECML workshop on Detection and Identification of Rare Audiovisual Cues (DIRAC), 24 Sept 2010, Barcelona, Spain.
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 [Weinshall et al NIPS 2009] 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.