Machine learning: paradigms and methods
Machine learning: paradigms and methods
Models of incremental concept formation
Machine learning: paradigms and methods
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
An APRIORI-based Method for Frequent Composite Event Discovery in Videos
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Joint Recognition of Complex Events and Track Matching
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
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We propose an event learning approach for video, based on concept formation models. This approach incrementally learns on-line a hierarchy of states and event by aggregating the attribute values of tracked objects in the scene. The model can aggregate both numerical and symbolic values. The utilisation of symbolic attributes gives high flexibility to the approach. The approach also proposes the integration of attributes as a doublet value-reliability, for considering the effect in the event learning process of the uncertainty inherited from previous phases of the video analysis process. Simultaneously, the approach recognises the states and events of the tracked objects, giving a multi-level description of the object situation. The approach has been evaluated for an elderly care application and a rat behaviour analysis application. The results show that the approach is capable of learning and recognising meaningful events occurring in the scene, and to build a rich model of the objects behaviour. The results also show that the approach can give 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.