Recognition of aggressive human behavior using binary local motion descriptors

Video surveillance is an alternative approach to staff or self-reporting that has the potential to detect and monitor aggressive behaviors more accurately. In this paper, we propose an automatic algorithm capable of recognizing aggressive behaviors from video records using local binary motion descriptors. The proposed algorithm may increase the accuracy for retrieving aggressive behaviors from video records, and thereby facilitates scientific inquiry into this low frequency but high impact phenomenon that eludes other measurement approaches.

[1]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Ivan Laptev,et al.  On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  Alexander Zien,et al.  Probabilistic Semi-Supervised Clustering with Constraints , 2006 .

[4]  Arindam Banerjee,et al.  Probabilistic Semi-Supervised Clustering with Constraints , 2006, Semi-Supervised Learning.

[5]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Martial Hebert,et al.  Efficient visual event detection using volumetric features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[7]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  R. Nelson,et al.  Low level recognition of human motion (or how to get your man without finding his body parts) , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[9]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[10]  Wang Xiaodan,et al.  Approach of automatic multithreshold image segmentation based on class variance , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[11]  Ming-yu Chen,et al.  Long Term Activity Analysis in Surveillance Video Archives , 2010 .

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

[13]  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.

[14]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[15]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[16]  Koichiro Akita,et al.  Image sequence analysis of real world human motion , 1984, Pattern Recognit..

[17]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2006, BMVC.

[19]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[20]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  David C. Hogg.  Model-based vision: a program to see a walking person , 1983, Image Vis. Comput..

[22]  Soon Ki Jung,et al.  Particle filter with analytical inference for human body tracking , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[23]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..