Video Event Detection Based Non-stationary Bayesian Networks

• Published in: Advanced Concepts for Intelligent Vision Systems Vol. 10016; pp. 419 - 430
• Main Authors: Gonzales, Christophe, Romdhane, Rim, Dubuisson, Séverine
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Dynamic Bayesian Networks (DBN); Multiple Event Detection; Particle Filter (PF); Particle Set; Video Sequences
• ISBN: 9783319486796; 3319486799
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-48680-2_37

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).