This paper presents a new approach to crowd behaviour anomaly detection that uses a set of efficiently computed, easily interpretable, scene-level holistic features. This low-dimensional descriptor combines two features from the literature: crowd collectiveness [1] and crowd conflict [2], with two newly developed crowd features: mean motion speed and a new formulation of crowd density. Two different anomaly detection approaches are investigated using these features. When only normal training data is available we use a Gaussian Mixture Model (GMM) for outlier detection. When both normal and abnormal training data is available we use a Support Vector Machine (SVM) for binary classification. We evaluate on two crowd behaviour anomaly detection datasets, achieving both state-of-the-art classification performance on the violent-flows dataset [3] as well as better than real-time processing performance (40 frames per second).
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Publication Type = Conference or Workshop Item
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Subject = Computer Science: Image processing
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Status = Published
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Electronic Engineering
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DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
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Dublin City University ->
Subject = Computer Science: Machine learning
Noel E. O'Connor,
Suzanne Little,
Kevin McGuinness,
Mark Marsden