Video Event Detection Based Non-stationary Bayesian Networks

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).