Abnormal Behavior Detection Algorithm Based on Sparse Overcomplete Representation
[1]Lu Peng,Zhang Lia,Huang Shilei,et al.Abnormal Behavior Detection Algorithm Based on Sparse Overcomplete Representation[J].Journal of Zhengzhou University (Engineering Science),2016,37(06):72-76.[doi:10.13705/j.issn.1671-6833.2016.03.031]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
37
Number of periods:
2016 06
Page number:
72-76
Column:
Public date:
2016-12-31
- Title:
- Abnormal Behavior Detection Algorithm Based on Sparse Overcomplete Representation
- Keywords:
-
abnormal behavior detection; sparse overcomplete representation; spatial temporal interesting points
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2016.03.031
- Abstract:
- The focus question of video abnormal behavior detection was how to illustrate the behavior correctly through analysis of huge amounts of data. A new algorithm was proposed based on visual sparse overcomplete representation mechanism to extract local effective information about the interest points in the video of specific scenario, which could improve the efficiency of data processing. Firstly, the algorithm extracted the local spa-tial temporal interesting points ( STIP) in training samples. At the same time it calculated the local spatial temporal characteristics. Then it put the characteristics into sparse overcomplete representation model to get a set of sparse matrix after training. Finally, it reconstructed the query video using the aforementioned matrix to detect abnormal behavior through the reconstruction error of local spatial temporal characteristics. In addition, the updated algorithm of sparse matrix function for different videos was proposed. Experiment results on stand-ard database showed that our algorithm could detect abnormal behavior effectively and with higher accuracy and lower false alarm.
- References:
- -
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