Snoop: an expressive event specification language for active databases
Data & Knowledge Engineering
Composite Event Specification in Active Databases: Model & Implementation
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
Inference of Security Hazards from Event Composition Based on Incomplete or Uncertain Information
IEEE Transactions on Knowledge and Data Engineering
Event modelling and reasoning with uncertain information for distributed sensor networks
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Handling sequential observations in intelligent surveillance
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Real-time vandalism detection by monitoring object activities
Multimedia Tools and Applications
A characteristic function approach to inconsistency measures for knowledge bases
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Evidential fusion for gender profiling
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
International Journal of Multimedia Data Engineering & Management
An ambiguity aversion framework of security games under ambiguities
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Demand for bus surveillance is growing due to the increased threats of terrorist attack, vandalism and litigation. However, CCTV systems are traditionally used in forensic mode, precluding an in-time reaction to an event. In this paper, we introduce a real-time event composition framework which can support the instant recognition of emergent events based on uncertain or imperfect information gathered from multiple sources. This framework deploys a rule-based reasoning component that can infer malicious situations (composite events) from a set of correlated atomic events. These are recognized by applying analytic algorithms to the multimedia contents of bus surveillance data. We demonstrate the significance and usefulness of our framework with a case study of an on-going bus surveillance project.