Abnormal event detection based on local topology and l<sub>1/2</sub>norm regularize
A new dictionary learning method was proposed by introducing a local topology term to describe structural information of video events and using the l<sub>1/2</sub>norm as the sparsity constraint to the representation coefficients based on the traditional analysis dictionary learning meth...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2018-10-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2018254/ |
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| Summary: | A new dictionary learning method was proposed by introducing a local topology term to describe structural information of video events and using the l<sub>1/2</sub>norm as the sparsity constraint to the representation coefficients based on the traditional analysis dictionary learning method.In feature extraction,a histogram of interaction force(HOIF) containing rich motion information and a histogram of oriented gradient(HOG) containing texture information were merged.Then,the improved dictionary was used to train the feature data.Finally,the reconstruction error of the testing sample under the dictionary was used to determine whether the testing sample was an abnormal sample.Experiments on UMN show the high performance of the algorithm.Compared with the state-of-the-art algorithms,the analysis dictionary classification algorithm based on local topology and l<sub>1/2</sub>norm has made more effective detection on the abnormal events in the crowd. |
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| ISSN: | 1000-0801 |