Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2013-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/13/12/17130 |
| _version_ | 1798005718927802368 |
|---|---|
| author | Tian Wang Jie Chen Yi Zhou Hichem Snoussi |
| author_facet | Tian Wang Jie Chen Yi Zhou Hichem Snoussi |
| author_sort | Tian Wang |
| collection | DOAJ |
| description | The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method. |
| first_indexed | 2024-04-11T12:43:32Z |
| format | Article |
| id | doaj.art-d666423191874272a0debe2f039dc0dd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:43:32Z |
| publishDate | 2013-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d666423191874272a0debe2f039dc0dd2022-12-22T04:23:25ZengMDPI AGSensors1424-82202013-12-011312171301715510.3390/s131217130s131217130Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event DetectionTian Wang0Jie Chen1Yi Zhou2Hichem Snoussi3Institut Charles Delaunay-LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, Troyes 10004, FranceObservatoire de la Côte d'Azur-UMR 7293 CNRS, University of Nice Sophia-Antipolis, Nice 06108, FranceCollege of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaInstitut Charles Delaunay-LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, Troyes 10004, FranceThe abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.http://www.mdpi.com/1424-8220/13/12/17130abnormal detectionoptical flowcovariance matrix descriptoronline least squares one-class SVM |
| spellingShingle | Tian Wang Jie Chen Yi Zhou Hichem Snoussi Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection Sensors abnormal detection optical flow covariance matrix descriptor online least squares one-class SVM |
| title | Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection |
| title_full | Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection |
| title_fullStr | Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection |
| title_full_unstemmed | Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection |
| title_short | Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection |
| title_sort | online least squares one class support vector machines based abnormal visual event detection |
| topic | abnormal detection optical flow covariance matrix descriptor online least squares one-class SVM |
| url | http://www.mdpi.com/1424-8220/13/12/17130 |
| work_keys_str_mv | AT tianwang onlineleastsquaresoneclasssupportvectormachinesbasedabnormalvisualeventdetection AT jiechen onlineleastsquaresoneclasssupportvectormachinesbasedabnormalvisualeventdetection AT yizhou onlineleastsquaresoneclasssupportvectormachinesbasedabnormalvisualeventdetection AT hichemsnoussi onlineleastsquaresoneclasssupportvectormachinesbasedabnormalvisualeventdetection |