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- W65642710 abstract "In this thesis, we show that automatic event understanding from video streams may be achieved for a large class of events based on the observation of the trajectories and the shapes of objects. We propose a new formalism, in which events are described by a hierarchical representation consisting of “image features”, “mobile object properties” and “event scenarios”. Taking image features of tracked moving regions from an image sequence as input, mobile object properties are first computed by specific methods while noise is suppressed by statistical methods. Events are viewed as consisting of “single” or “multiple” threads. In a single thread event, relevant actions occur along a linear time scale. They are recognized from mobile object properties using Bayesian networks and stochastic finite automata. For multiple thread events, several threads of events are related by logical and temporal constraints. These constraints are verified in a probabilistic framework where all possible durations are considered for an optimal estimate. An Event Recognition Language is proposed for describing these events in a natural way. This particular design is based on our intention to be able to describe events at the symbolic level as well as to provide the optimal recognition of these events from low level visual facts in presence of variations in event execution styles and tracking noise, native to the analysis of real image sequences." @default.
- W65642710 created "2016-06-24" @default.
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- W65642710 date "2003-01-01" @default.
- W65642710 modified "2023-09-28" @default.
- W65642710 title "A unified bayesian and logical approach for video-based event recognition" @default.
- W65642710 hasPublicationYear "2003" @default.
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