journal paper


JournalIEEE Transactions on Information Forensics and Security
VolumePP
Issue/Number99
PublisherIEEE
Pages1--12
Date11.07.2017
Author(s)Tobias Senst, Volker Eiselein, Alexander Kuhn, Thomas Sikora
TitleCrowd Violence Detection Using Global Motion-Compensated Lagrangian Features and Scale-Sensitive Video-Level Representation
AbstractLagrangian 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.
Key wordsviolence detection, lagrangian theory, lagrangian measures, crowd analysis, local feature, action recognition, longterm motion
NotePrint ISSN: 1556-6013
Online ISSN: 1556-6021
www.doi.org/10.1109/TIFS.2017.2725820
File1512Senst2017.pdf

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