Video Event Detection Based Non-stationary Bayesian Networks - Gonzales, Christophe; Romdhane, Rim; Dubuisson, Séverine (Advanced Concepts for Intelligent Vision Systems, 2016)
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).