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- W2534875948 abstract "In this paper, we propose an approach for detecting events online in video sequences. This one requires no prior knowledge, the events being defined as spatio-temporal breaks. For this purpose, we propose to combine non-stationary dynamic Bayesian networks (nsDBN) to model the scene and particle filter (PF) to track objects in the sequence. In this framework, an event corresponds to a significant difference between a new particle set provided by PF and the sampled density encoded by the nsDBN. Whenever an event is detected, the particle set is exploited to learn a new nsDBN representing the scene. Unfortunately, nsDBNs are designed for discrete random variables and particles are instantiations of continuous ones. We therefore propose to discretize them using a new discretization method well suited for nsDBNs. Our approach has been tested on real video sequences and allowed to detect two different events (forbidden stop and fight)." @default.
- W2534875948 created "2016-10-28" @default.
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- W2534875948 date "2016-01-01" @default.
- W2534875948 modified "2023-10-15" @default.
- W2534875948 title "Video Event Detection Based Non-stationary Bayesian Networks" @default.
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- W2534875948 doi "https://doi.org/10.1007/978-3-319-48680-2_37" @default.
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