Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Video event detection using motion relativity and visual relatedness
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Detecting Abnormal Events via Hierarchical Dirichlet Processes
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Video activity recognition in the real world
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A double layer background model to detect unusual events
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Proceedings of the 6th international conference on Human-robot interaction
Hierarchical visual event pattern mining and its applications
Data Mining and Knowledge Discovery
Detecting anomalies in people's trajectories using spectral graph analysis
Computer Vision and Image Understanding
A comprehensive study of visual event computing
Multimedia Tools and Applications
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Weighted interaction force estimation for abnormality detection in crowd scenes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Computer Vision and Image Understanding
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We present a new approach for recognizing rare events in aerial video. We use the framework of Hidden Markov Models (HMMs) to represent the spatio-temporal relations between objects and uncertainty in observations, where the data observables are semantic spatial primitives encoded based on prior knowledge about the events of interest. Events are observed as a sequence of binarized distance relations among the objects participating in the event. This avoids directly modeling the temporal trajectories of continuous observables, which is difficult when training data is scarce. The approach enables better generalization to other scenes for which little or no training data may be available. We demonstrate the effectiveness of our approach using real aerial video and simulated data.