Effective Scheme for Global Abnormal Event Detection for Surveillance Video

DOI

10.2991/cimns-16.2016.51How to use a DOI?

Keywords

abnormal detection; illumination change; binary images; sparse reconstruction cost

Abstract

An effective algorithm for global abnormal detection from surveillance video is proposed in this paper. The algorithm is based on sparse representation. To deal with the illumination change in video scenes, specific feature extract methods are designed for corresponding illumination conditions. In the case of non-uniform illumination, features are extracted directly on the original image; in the case of uniform illumination, features are extracted on the binary image obtained by threshold segmentation on the difference image, where the thresholds are computed by the Otsu's method. The features extracted on normal video are used to learn an over-complete dictionary. Then, the sparse reconstruction cost over the dictionary is used to detect abnormal events. Experiments on the open global abnormal dataset and the comparison to the state-of-the-art methods validate effectiveness and quickness of our algorithm.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Fangxu Dong
AU  - Dong Hu
PY  - 2016/09
DA  - 2016/09
TI  - Effective Scheme for Global Abnormal Event Detection for Surveillance Video
BT  - Proceedings of the 2016 International Conference on Communications, Information Management and Network Security
PB  - Atlantis Press
SP  - 205
EP  - 209
SN  - 2352-538X
UR  - https://doi.org/10.2991/cimns-16.2016.51
DO  - 10.2991/cimns-16.2016.51
ID  - Dong2016/09
ER  -