Detection and Identification of Rare Audiovisual Cues

Volume 384 of the series Studies in Computational Intelligence pp 109-117

Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation

  • I. AlmajaiAffiliated withCVSSP, University of Surrey
  • , F. YanAffiliated withCVSSP, University of Surrey
  • , T. de CamposAffiliated withCVSSP, University of Surrey
  • , A. KhanAffiliated withCVSSP, University of Surrey
  • , W. ChristmasAffiliated withCVSSP, University of Surrey
  • , D. WindridgeAffiliated withCVSSP, University of Surrey
  • , J. KittlerAffiliated withCVSSP, University of Surrey

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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.