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Crowd Violence Detection Using Global Motion-Compensated Lagrangian Features and Scale-Sensitive Video-Level Representation
Citation key 1512Senst2017
Author Tobias Senst and Volker Eiselein and Alexander Kuhn and Thomas Sikora
Pages 2945–2956
Year 2017
Journal IEEE Transactions on Information Forensics and Security
Volume 12
Number 12
Month dec
Note Print ISSN: 1556-6013 Online ISSN: 1556-6021 www.doi.org/10.1109/TIFS.2017.2725820
Abstract Lagrangian theory provides a rich set of tools for analyzing non-local, long-term motion information in computer vision applications. Based on this theory, we present a specialized Lagrangian technique for the automated detection of violent scenes in video footage. We present a novel feature using Lagrangian direction fields that is based on a spatio-temporal model and uses appearance, background motion compensation, and long-term motion information. To ensure appropriate spatial and temporal feature scales, we apply an extended bag-of-words procedure in a late-fusion manner as classification scheme on a per-video basis.We demonstrate that the temporal scale, captured by the Lagrangian integration time parameter, is crucial for violence detection and show how it correlates to the spatial scale of characteristic events in the scene. The proposed system is validated on multiple public benchmarks and non-public, real-world data from the London Metropolitan Police. Our experiments confirm that the inclusion of Lagrangian measures is a valuable cue for automated violence detection and increases the classification performance considerably compared to stateof- the-art methods.
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