Models of incremental concept formation
Machine learning: paradigms and methods
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Functional Object-Categories from a Relational Spatio-Temporal Representation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Automatic video interpretation: a novel algorithm for temporal scenario recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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We propose a new approach for video event learning. The only hypothesis is the availability of tracked object attributes. The approach incrementally aggregates the attributes and reliability information of tracked objects to learn a hierarchy of state and event concepts. Simultaneously, the approach recognises the states and events of the tracked objects. This approach proposes an automatic bridge between the low-level image data and higher level conceptual information. The approach has been evaluated for more than two hours of an elderly care application. The results show the capability of the approach to learn and recognise meaningful events occurring in the scene. Also, the results show the potential of the approach for giving a description of the activities of a person (e.g. approaching to a table, crouching), and to detect abnormal events based on the frequency of occurrence.