Learning Prior and Observation Augmented Density Models forBehaviour Recognition
Michael A. H. B. Walter, Αλεξάνδρα Ψαρρού, Shufeng Gong · 1999
Recognition of human behaviours requires modeling the underlying spatial and temporal structures of their motion patterns. Such structures are intrinsically probabilistic and therefore should be modelled as stochastic processes. In this paper we introduce a framework to recognise behaviours based on both learning prior and continuous propagation of density models of behaviour patterns. Prior is learned from training sequences using hidden Markov models and density models are augmented by current visual observation.