Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes
Abnormal events detection plays an important role in the video surveillance, which is a challenging subject in the intelligent detection. In this paper, based on a novel motion feature descriptor, that is, the histogram of maximal optical flow projection (HMOFP), we propose an algorithm to detect ab...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/406941 |
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| _version_ | 1850160301421363200 |
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| author | Ang Li Zhenjiang Miao Yigang Cen Tian Wang Viacheslav Voronin |
| author_facet | Ang Li Zhenjiang Miao Yigang Cen Tian Wang Viacheslav Voronin |
| author_sort | Ang Li |
| collection | DOAJ |
| description | Abnormal events detection plays an important role in the video surveillance, which is a challenging subject in the intelligent detection. In this paper, based on a novel motion feature descriptor, that is, the histogram of maximal optical flow projection (HMOFP), we propose an algorithm to detect abnormal events in crowded scenes. Following the extraction of the HMOFP of the training frames, the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of the testing frames. Compared with other methods based on the optical flow, experiments on several benchmark datasets show that our algorithm is effective with satisfying results. |
| format | Article |
| id | doaj-art-b6d4d818faf345f6907f76f00fe94d46 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-b6d4d818faf345f6907f76f00fe94d462025-08-20T02:23:11ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-11-011110.1155/2015/406941406941Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded ScenesAng Li0Zhenjiang Miao1Yigang Cen2Tian Wang3Viacheslav Voronin4 Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China Department of Radio-Electronic Systems, Don State Technical University, Shakhty 346500, RussiaAbnormal events detection plays an important role in the video surveillance, which is a challenging subject in the intelligent detection. In this paper, based on a novel motion feature descriptor, that is, the histogram of maximal optical flow projection (HMOFP), we propose an algorithm to detect abnormal events in crowded scenes. Following the extraction of the HMOFP of the training frames, the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of the testing frames. Compared with other methods based on the optical flow, experiments on several benchmark datasets show that our algorithm is effective with satisfying results.https://doi.org/10.1155/2015/406941 |
| spellingShingle | Ang Li Zhenjiang Miao Yigang Cen Tian Wang Viacheslav Voronin Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes International Journal of Distributed Sensor Networks |
| title | Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes |
| title_full | Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes |
| title_fullStr | Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes |
| title_full_unstemmed | Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes |
| title_short | Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes |
| title_sort | histogram of maximal optical flow projection for abnormal events detection in crowded scenes |
| url | https://doi.org/10.1155/2015/406941 |
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