Learning spatial context from tracking using penalised likelihoods

Stephen J. McKenna, Hammadi Nait-Charif

    Research output: Contribution to journalArticlepeer-review

    22 Citations (Scopus)


    MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. This enabled prior beliefs about the scale, orientation and elongation of semantic regions to be encoded, encouraging one-to-one correspondences between mixture components and these regions. In conjunction with minimum description length this enabled automatic learning of inactivity zones and entry zones from track data in a supportive home environment.
    Original languageEnglish
    Pages (from-to)138-141
    Number of pages4
    JournalProceedings - International Conference on Pattern Recognition
    Publication statusPublished - 1 Jan 2004


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