Automatic Acquisition of Context Models and its Application to Video Surveillance
Abstract
This paper addresses the problem of automatically acquiring context models from data. Context and human behavior are represented using a state model, called situation model. This model consists of different layers referring to entities, filters, roles, relations, situation and situation relationship. We propose a framework for the automatic acquisition of these different layers. In particular, this paper proposes a novel generic situation acquisition algorithm. The algorithm is also successfully applied to a video surveillance task and is evaluated by the public CAVIAR video database. The results are encouraging
Keywords
Bridges
Context modeling
Databases
Feedback
Filtering
Filters
Humans
Sensor systems
Testing
image representation
surveillance
video signal processing
CAVIAR video database
automatic context model acquisition
context representation
data context model
human behavior representation
situation acquisition algorithm
situation model
state model
video surveillance