Real-time Multiple Abnormality Detection in Video Data

Simon Hartmann Have, Huamin Ren, Thomas B. Moeslund

2013

Abstract

Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.

References

  1. (2008). Ucsd anomaly dataset. http://www.svcl.ucsd.edu/ projects/anomaly/dataset.html.
  2. (2012). Maryland department of transportation. http:// www.traffic.md.gov/.
  3. Boslaugh, S. and Watters, P. (2008). Statistics in a Nutshell. O'Reilly Media, Inc.
  4. Bouguet, J. (2000). Pyramidal implementation of the lucas kanade feature tracker description of the algorithm.
  5. Cui, X., Liu, Q., Gao, M., and Metaxas, D. (2011). Abnormal detection using interaction energy potentials. In CVPR.
  6. Horn, B. and Schunck, B. (1981). Determining optical flow. Artificial Intelligence.
  7. Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., and Maybank, S. (2006). A system for learning statistical motion patterns. PAMI.
  8. Johnson, N. and Hogg, D. (1995). Learning the distribution of object trajectories for event recognition. In British conference on Machine vision.
  9. Kim, J. and Grauman, K. (2009). Observe locally, infer globally: A space-time mrf for detecting abnormal activities with incremental updates. In CVPR.
  10. Lee, T. (1996). Image representation using 2d gabor wavelets. PAMI, 18:959-971.
  11. Li, L., W. Huang, I. G., and Tian, Q. (2003). Foreground object detection from videos containing complex background. In ACM international conference on Multimedia.
  12. Lucas, B. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In International joint conference on Artificial intelligence.
  13. Mahadevan, V., Li, W., Bhalodia, V., and Vasconcelos, N. (2010). Anomaly detection in crowded scenes. In CVPR.
  14. Mehran, R., Oyama, A., and Shah, M. (2009). Abnormal crowd behavior detection using social force model. In CVPR.
  15. Piciarelli, C. and Foresti, G. L. (2006). On-line trajectory clustering for anomalous events detection. Pattern Recognition Letters, 27(15):1835-1842.
  16. Reddy, V., Sanderson, C., and Lovell, B. (2011). Improved anomaly detection in crowded scenes via cell-based analysis foreground speed, size and texture. In International Workshop on Machine Learning for Visionbased Motion Analysis (CVPRW).
  17. Stauffer, C. and Grimson, W. (2000). Learning patterns of activity using real-time tracking. PAMI, 22(8):747- 757.
  18. Yu, Q. and Medioni, G. (2009). Motion pattern interpretation and detection for tracking moving vehicles in airborne video. In CVPR.
  19. Zhao, B., Li, F., and Xing, E. (2011). Online detection of unusual events in videos via dynamic sparse coding. In CVPR.
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Paper Citation


in Harvard Style

Have S., Ren H. and Moeslund T. (2013). Real-time Multiple Abnormality Detection in Video Data . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 390-395. DOI: 10.5220/0004280703900395


in Bibtex Style

@conference{visapp13,
author={Simon Hartmann Have and Huamin Ren and Thomas B. Moeslund},
title={Real-time Multiple Abnormality Detection in Video Data},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={390-395},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004280703900395},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Real-time Multiple Abnormality Detection in Video Data
SN - 978-989-8565-48-8
AU - Have S.
AU - Ren H.
AU - Moeslund T.
PY - 2013
SP - 390
EP - 395
DO - 10.5220/0004280703900395