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

DOI10.1016/J.PATCOG.2009.04.025zbMath1191.68582OpenAlexW2054846008MaRDI QIDQ733156

Shehzad Khalid

Publication date: 15 October 2009

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2009.04.025

zbMATH Keywords

dimensionality reductiontrajectory clusteringanomaly detectionevent miningmotion recognitionobject trajectorytrajectory modelling

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (3)

TOBAE: a density-based agglomerative clustering algorithm ⋮ Frameworks for multivariate \(m\)-mediods based modeling and classification in Euclidean and general feature spaces ⋮ Activity classification and anomaly detection using \(m\)-mediods based modelling of motion patterns

Uses Software

• apcluster
• APCluster

Cites Work

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• Clustering by Passing Messages Between Data Points
• Unsupervised Curve Clustering using B‐Splines
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

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