Activity classification and anomaly detection using \(m\)-mediods based modelling of motion patterns

DOI10.1016/J.PATCOG.2010.05.006zbMath1213.68532OpenAlexW2064320675MaRDI QIDQ991992

Shehzad Khalid

Publication date: 8 September 2010

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2010.05.006

zbMATH Keywords

dimensionality reductionanomaly detectionevent miningmotion recognitionobject trajectorytrajectory modelling

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (2)

TOBAE: a density-based agglomerative clustering algorithm ⋮ Frameworks for multivariate \(m\)-mediods based modeling and classification in Euclidean and general feature spaces

Cites Work

• Motion-based behaviour learning, profiling and classification in the presence of anomalies
• Unnamed Item

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