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An Intelligent Method For Violence Detection in Live Video Feeds

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Date Issued:
2016
Summary:
In the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection of live video, in addition to prerecorded video. The results for violence detection in prerecorded videos are comparable to other popular detection systems and the results for live video are also very encouraging, making the work proposed in this thesis a solid foundation for improved live violence detection systems.
Title: An Intelligent Method For Violence Detection in Live Video Feeds.
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Name(s): Eneim, Maryam, author
Marques, Oge, Thesis advisor
Florida Atlantic University, Degree grantor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2016
Date Issued: 2016
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 55 p.
Language(s): English
Summary: In the past few years, violence detection has become an increasingly rele- vant topic in computer vision with many proposed solutions by researchers. This thesis proposes a solution called Criminal Aggression Recognition Engine (CARE), an OpenCV based Java implementation of a violence detection system that can be trained with video datasets to classify action in a live feed as non-violent or violent. The algorithm extends existing work on fast ght detection by implementing violence detection of live video, in addition to prerecorded video. The results for violence detection in prerecorded videos are comparable to other popular detection systems and the results for live video are also very encouraging, making the work proposed in this thesis a solid foundation for improved live violence detection systems.
Identifier: FA00004775 (IID)
Degree granted: Thesis (M.S.)--Florida Atlantic University, 2016.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Multimedia systems.
Image analysis.
Computer vision.
Visual communication--Social aspects.
Social problems--21st century.
Pattern recognition systems.
Optical pattern recognition.
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Links: http://purl.flvc.org/fau/fd/FA00004775
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00004775
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.