Real-time abnormal motion detection in surveillance video

Video surveillance systems produce huge amounts of data for storage and display. Long-term human monitoring of the acquired video is impractical and ineffective. Automatic abnormal motion detection system which can effectively attract operator attention and trigger recording is therefore the key to successful video surveillance in dynamic scenes, such as airport terminals. This paper presents a novel solution for real-time abnormal motion detection. The proposed method is well-suited for modern video-surveillance architectures, where limited computing power is available near the camera for compression and communication. The algorithm uses the macroblock motion vectors that are generated in any case as part of the video compression process. Motion features are derived from the motion vectors. The statistical distribution of these features during normal activity is estimated by training. At the operational stage, improbable-motion feature values indicate abnormal motion. Experimental results demonstrate reliable real-time operation.

[1]  Ankur Teredesai,et al.  VENUS: A System for Novelty Detection in Video Streams with Learning , 2004, FLAIRS.

[2]  Jianbo Shi,et al.  Detecting unusual activity in video , 2004, CVPR 2004.

[3]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[4]  Randal C. Nelson,et al.  Detection and Recognition of Periodic, Nonrigid Motion , 1997, International Journal of Computer Vision.

[5]  Ramakant Nevatia,et al.  Event Detection and Analysis from Video Streams , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Michal Irani,et al.  Detecting Irregularities in Images and in Video , 2005, ICCV.

[8]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Konstantinos Konstantinides,et al.  Image and Video Compression Standards: Algorithms and Architectures , 1997 .

[10]  Rama Chellappa,et al.  Role of shape and kinematics in human movement analysis , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[11]  Jitendra Malik,et al.  Recognizing action at a distance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Wayne H. Wolf,et al.  Human activity detection in MPEG sequences , 2000, Proceedings Workshop on Human Motion.

[13]  Shaogang Gong,et al.  Beyond Tracking: Modelling Activity and Understanding Behaviour , 2006, International Journal of Computer Vision.

[14]  Horst Bischof,et al.  Tracking multiple humans by fast mean shift mode seeking , 2005 .

[15]  Michael J. Black,et al.  Parameterized modeling and recognition of activities , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[16]  Mubarak Shah,et al.  View-Invariant Representation and Recognition of Actions , 2002, International Journal of Computer Vision.

[17]  Shaogang Gong,et al.  VIGOUR: a system for tracking and recognition of multiple people and their activities , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[18]  Michael J. Black,et al.  Parameterized Modeling and Recognition of Activities , 1999, Comput. Vis. Image Underst..

[19]  Yan Huang,et al.  ARGMode - Activity Recognition using Graphical Models , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[20]  R. Mattone,et al.  Evaluation of a Self-learning Event Detector , 2002 .

[21]  P. S. Sastry,et al.  Abnormal activity detection in video sequences using learnt probability densities , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.