Representing heuristic knowledge in D-S theory
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Snoop: an expressive event specification language for active databases
Data & Knowledge Engineering
Active Rules in Database Systems
Active Rules in Database Systems
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
Complex event processing over uncertain data
Proceedings of the second international conference on Distributed event-based systems
Inference of Security Hazards from Event Composition Based on Incomplete or Uncertain Information
IEEE Transactions on Knowledge and Data Engineering
Event Composition with Imperfect Information for Bus Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Handling sequential observations in intelligent surveillance
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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
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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CCTV and sensor based surveillance systems are part of our daily lives now in thismodern society due to the advances in telecommunications technology and the demand for better security. The analysis of sensor data produces semantic rich events describing activities and behaviours of objects being monitored. Three issues usually are associated with events descriptions. First, data could be collected from multiple sources (e.g., sensors, CCTVs, speedometers, etc). Second, descriptions about these data can be poor, inaccurate or uncertain when they are gathered from unreliable sensors or generated by analysis non-perfect algorithms. Third, in such systems, there is a need to incorporate domain specific knowledge, e.g., criminal statistics about certain areas or patterns, when making inferences. However, in the literature, these three phenomena are seldom considered in CCTV-based event composition models. To overcome these weaknesses, in this paper, we propose a general event modelling and reasoning model which can represent and reason with events from multiple sources including domain knowledge, integrating the Dempster-Shafer theory for dealing with uncertainty and incompleteness.We introduce a notion called event cluster to represent uncertain and incomplete events induced from an observation. Event clusters are then used in the merging and inference process. Furthermore, we provide a method to calculate the mass values of events which use evidential mapping techniques.