Abstract
Using a visual scene object tracker and a Non-Axiomatic Reasoning System we demonstrate how to predict and detect various anomaly classes. The approach combines an object tracker with a base ontology and the OpenNARS reasoner to learn to classify scene regions based on accumulating evidence from typical entity class (tracked object) behaviours. The system can autonomously satisfy goals related to anomaly detection and respond to user Q&A in real time. The system learns directly from experience with no initial training required (one-shot). The solution is a fusion of neural techniques (object tracker) and a reasoning system (with ontology).
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- 1.
This subsection was adapted from [12] to make the paper self-contained.
- 2.
This treatment is similar to the set-theoretic definition of “relation” as set of tuples, where it is also possible to define what is related to a given element in the relation as a set. For detailed discussions, see [10].
- 3.
The definitions of disjunction and conjunction in propositional logic do not require the components to be related in content, which lead to various issues under AIKR. In NARS, such a compound is formed only when the components are related semantically, temporally, or spatially. See [10] for details.
- 4.
Here the direction of the arrowhead is the direction of the implication relation, while the direction of the slash is the direction of the temporal order. In principle, copulas like ‘\(/\!\!\!\Leftarrow \)’ can also be defined, but they will be redundant. For more discussion on this topic, see [10].
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Hammer, P., Lofthouse, T., Fenoglio, E., Latapie, H., Wang, P. (2021). A Reasoning Based Model for Anomaly Detection in the Smart City Domain. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_13
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DOI: https://doi.org/10.1007/978-3-030-55187-2_13
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