Artificial intelligence and mathematical theory of computation
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Knowledge, action, and the frame problem
Artificial Intelligence
A Dynamic Bayesian Network Approach to Multi-cue based Visual Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Projection using regression and sensors
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Action representation and partially observable planning using epistemic logic
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
The frame problem and knowledge-producing actions
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
A semantic characterization of a useful fragment of the situation calculus with knowledge
Artificial Intelligence
Iterated belief change in the situation calculus
Artificial Intelligence
Intelligent Sensor Information System For Public Transport - To Safely Go
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Event modelling and reasoning with uncertain information for distributed sensor networks
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Evidential fusion for gender profiling
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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Demand for intelligent surveillance in public transport systems is growing due to the increased threats of terrorist attack, vandalism and litigation. The aim of intelligent surveillance is in-time reaction to information received from various monitoring devices, especially CCTV systems. However, video analytic algorithms can only provide static assertions, whilst in reality,many related events happen in sequence and hence should be modeled sequentially. Moreover, analytic algorithms are error-prone, hence how to correct the sequential analytic results based on new evidence (external information or later sensing discovery) becomes an interesting issue. In this paper, we introduce a high-level sequential observation modeling framework which can support revision and update on new evidence. This framework adapts the situation calculus to deal with uncertainty from analytic results. The output of the framework can serve as a foundation for event composition. We demonstrate the significance and usefulness of our framework with a case study of a bus surveillance project.