Terrorist Video Detection Using Visual Semantic Concepts

doi:10.3969/j.issn.1671-1122.2016.09.003

Abstract

Abstract:

The spreading of terrorist video on internet threated the public safety a lot, and the detection of terrorist video content is full of technical challenges. In this paper, a terrorist video dataset was constructed for evaluation of algorithms, and the dataset is annotated by visual semantic concepts. Methods based on five kind of feature descriptor (gray histogram, color histogram, color moment, local binary pattern and histogram of orientation of gradient) and support vector machine and extreme learning machine were studied for visual semantic concept detection. A video key frame extraction algorithm based on gray massive center was implemented, and shots gradual change and sudden change were detected using similarity between neighboring frames and video sequence. A terrorist video detection framework was proposed combined visual semantic concepts and bag of visual semantic concepts, and the result of simulation experiment proved the effectiveness of it.

Key words: support vector machine, extreme learning machine, visual semantic concept detection, terrorist video detection

CLC Number:

TP309

Wei SONG, Pei YANG, Jing YU, Wei JIANG. Terrorist Video Detection Using Visual Semantic Concepts[J]. Netinfo Security, 2016, 16(9): 12-17.

Figures/Tables 7

References 23

[1]	中国互联网信息中心(CNNIC), 2016-09-12.
[2]	Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, , 2013-2018.
[3]	CONG Y, YUAN J, LIU J.Abnormal Event Detection in Crowded Scenes Using Sparse Representation[J]. Pattern Recognition, 2013(46): 1851-1864.
[4]	KARPATHY A, ARPATHY A, TODERICI G, et al.Large-scale Video Classification with Convolutional Neural Networks[C]// IEEE. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 23-28, Columbus, OH, USA. New York: IEEE, 2014:1725-1732.
[5]	BLANK M, GORELICK L, SHECHTMAN E, et al.Actions as Space-Time Shapes[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29(12):2247-53.
[6]	YILMAZ A, SHAH M.Actions sketch: A Novel Action Representation[C]// IEEE. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 20-25. San Diego, CA, USA. New York: IEEE, 2005: 984-989.
[7]	HASSNER T, ITCHER Y, KLIPER-GROSS O.Violent flows: Real-time Detection of Violent Crowd Behavior[C] //IEEE. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 16-21,Providence, RI USA. New York: IEEE, 2012: 1-6.
[8]	de Souza F D M, Chávez G C, do Valle E A, et al. Violence Detection in Video Using Spatio-temporal Features[C]// IEEE. IEEE 23th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), August 30 - September 3, Gramado, Brazil. New York: IEEE, 2010: 224-230.
[9]	NIEVAS E B, SUAREZ O D, GARCIA G B, et al.Violence Detection in Video Using Computer Vision Techniques[C]// Computer Analysis of Images and Patterns, Springer Berlin Heidelberg. Berlin: Springer-Verlag, 2011: 332-339.
[10]	CHEN M Y, HAUPTMANN A, CHEN M Y, et al.MoSIFT: Recognizing Human Actions in Surveillance Videos[J]. Annals of Pharmacotherapy, 2009, 39(1):150-2.
[11]	XU L, GONG C, YANG J, et al.Violent Video Detection Based on MoSIFT Feature and Sparse Coding[C]//IEEE. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 4-9. Florence, Italy. New York: IEEE, 2014: 3538-3542.
[12]	GOTO S, AOKI T. TUDCL at MediaEval 2013 Violent Scenes Detection: Training with Multi-modal Features by MKL[EB/OL]. , 2016-9-2.
[13]	LAM V, LE D D, PHAN S, et al. NII-UIT at MediaEval 2013 Violent Scenes Detection Affect Task[EB/OL]. , 216-9-2.
[14]	DAI Q, TU J, SHI Z, et al. Fudan at MediaEval 2013: Violent Scenes Detection Using Motion Features and Part-Level Attributes[EB/OL]. , 2016-9-2.
[15]	LIU J, Yu Q, Javed O, et al.Video event recognition using concept attributes[C]//IEEE. IEEE Workshop on Applications of Computer Vision (WACV), Jan 17-18,Clearwater Beach, Florida. New York: IEEE, 2013: 339-346.
[16]	ASSARI S, ZAMIR A, SHAH M.Video Classification Using Semantic Concept Co-occurrences[C] // IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 2529-2536.
[17]	GOTO S, AOKI T.Violent Scenes Detection Using Mid-level Violence Clustering[J]. Computer Science, 2014: 283-296.
[18]	TAN C C, NGO C W. The Vireo Team at MediaEval 2013: Violent Scenes Detection by Mid-level Concepts Learnt from Youtube[EB/OL]. , 2016-9-2.
[19]	IONESCU B, SCHLUTER J, MIRONICA I, et al.A Naive Mid-level Concept-based Fusion Approach to Violence Detection in Hollywood Movies[C]// ACM. Proceedings of the 3rd ACM conference on International conference on multimedia retrieval, April 16-19, Dallas, Texas, USA. New York:ACM, 2013: 215-222.
[20]	YANG J, JIANG Y G, HAUPTMANN A G, et al.Evaluating Bag-of-visual-words Representations in Scene Classification[C]// ACM. Sigmm International Workshop on Multimedia Information Retrieval, September, 2007, Augsburg, Bavaria, Germany. New York:ACM, 2007:197-206.
[21]	DESELAERS T, PIMENIDIS L, NEY H.Bag-of-visual-words Models for Adult Image Classification and Filtering[C]// IEEE. International Conference on Pattern Recognition, December 8-11, Tampa Convection Center, Tampa, Florida, USA. New York:IEEE, 2008: 1-4.
[22]	DOS SANTOS J M, DE MOURA E S, DA SILVA A S, et al. A signature-based Bag of Visual Words Method for Image Indexing and Search[J]. Pattern Recognition Letters, 2015(65): 1-7.
[23]	ALDAVERT D, RUSINOL M, TOLEDO R, et al.A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting[J]. International Journal on Document Analysis and Recognition (IJDAR), 2015, 18(3): 223-234.