Detecting the Degree of Anomal in Security Video

@inproceedings{Sudo2007DetectingTD,
  title={Detecting the Degree of Anomal in Security Video},
  author={Kyoko Sudo and Tatsuya Osawa and Xiaojun Wu and Kaoru Wakabayashi and Takayuki Yasuno},
  booktitle={MVA},
  year={2007}
}
We have developed a method that can discriminate anomalous image sequences for more efficiently utilizing security videos. To match the wide popularity of security cameras, the method is independent of the camera setting environment and video contents. We use the spatio-temporal feature obtained by extracting the areas of change from the video. To create the input for the discrimination process, we reduce the dimensionality of the data by PCA. Discrimination is based on a 1-class SVM, which is… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-7 of 7 references

Detecting the Degree of Anomaly in Security Videos by using a Spatio - Temporal Feature of Change

S. Blunsden, R. Fisher
Proc . ICPR • 2006

Detecting the Degree of Anomaly in Security Videos by using a Spatio-Temporal Feature of Change,”in

K. Sudo, T. Osawa, K. Wakabayashi, T. Yasuno
Proceedings of the International Conference on Signal and Image Processing, • 2006
View 1 Excerpt

Modelling Crowd Scenes for Event Detection

18th International Conference on Pattern Recognition (ICPR'06) • 2006
View 1 Excerpt

Pannozzo: “A completely autonomous system that learns anomalous movements in advanced video surveillance applications

M. A. Mecocci
Proc. IEEE International Conference on Pattern Recognition, • 2005
View 1 Excerpt

Similarity based vehicle trajectory clustering and anomaly detection

IEEE International Conference on Image Processing 2005 • 2005
View 1 Excerpt

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