Violence Detection in Video Using Spatio-Temporal Features

In this paper we presented a violence detector built on the concept of visual codebooks using linear support vector machines. It differs from the existing works of violence detection in what concern the data representation, as none has considered local spatio-temporal features with bags of visual words. An evaluation of the importance of local spatio-temporal features for characterizing the multimedia content is conducted through the cross-validation method. The results obtained confirm that motion patterns are crucial to distinguish violence from regular activities in comparison with visual descriptors that rely solely on the space domain.

[1]  Stephen J. Maybank,et al.  The ADVISOR Visual Surveillance System , 2004 .

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[4]  Prospero C. Naval,et al.  DOVE : Detection of Movie Violence using Motion Intensity Analysis on Skin and Blood , 2006 .

[5]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[6]  Hermann Ney,et al.  Bag-of-visual-words models for adult image classification and filtering , 2008, 2008 19th International Conference on Pattern Recognition.

[7]  Chong-Wah Ngo,et al.  Evaluating bag-of-visual-words representations in scene classification , 2007, MIR '07.

[8]  Ivan Laptev,et al.  On Space-Time Interest Points , 2005, International Journal of Computer Vision.

[9]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[10]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[11]  Johannes D. Krijnders,et al.  CASSANDRA: audio-video sensor fusion for aggression detection , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[12]  Weiqiang Wang,et al.  Weakly-Supervised Violence Detection in Movies with Audio and Video Based Co-training , 2009, PCM.

[13]  Mubarak Shah,et al.  Person-on-person violence detection in video data , 2002, Object recognition supported by user interaction for service robots.

[14]  Sergios Theodoridis,et al.  Violence Content Classification Using Audio Features , 2006, SETN.

[15]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .