Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM Installations
Automated teller machines (ATM) are widely being used to carry out banking transactions and are becoming one of the necessities of everyday life. ATMs facilitate withdrawal, deposit, and transfer of money from one account to another round the clock. However, this convenience is marred by criminal ac...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/502737 |
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| _version_ | 1849307985241702400 |
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| author | Vikas Tripathi Durgaprasad Gangodkar Vivek Latta Ankush Mittal |
| author_facet | Vikas Tripathi Durgaprasad Gangodkar Vivek Latta Ankush Mittal |
| author_sort | Vikas Tripathi |
| collection | DOAJ |
| description | Automated teller machines (ATM) are widely being used to carry out banking transactions and are becoming one of the necessities of everyday life. ATMs facilitate withdrawal, deposit, and transfer of money from one account to another round the clock. However, this convenience is marred by criminal activities like money snatching and attack on customers, which are increasingly affecting the security of bank customers. In this paper, we propose a video based framework that efficiently identifies abnormal activities happening at the ATM installations and generates an alarm during any untoward incidence. The proposed
approach makes use of motion history image (MHI) and Hu moments to extract relevant features from video. Principle component analysis has been used to reduce the dimensionality of features and classification has
been carried out by using support vector machine. Analysis has been carried out on different video sequences by varying the window size of MHI. The proposed framework is able to distinguish the normal and
abnormal activities like money snatching, harm to the customer by virtue of fight, or attack on the customer with an average accuracy of 95.73%. |
| format | Article |
| id | doaj-art-1f9adaa566cf4b40bb4d52d16e698387 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-1f9adaa566cf4b40bb4d52d16e6983872025-08-20T03:54:34ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/502737502737Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM InstallationsVikas Tripathi0Durgaprasad Gangodkar1Vivek Latta2Ankush Mittal3Computer Science Department, Graphic Era University, Dehradun 248002, IndiaComputer Science Department, Graphic Era University, Dehradun 248002, IndiaComputer Science Department, Graphic Era University, Dehradun 248002, IndiaComputer Science Department, Graphic Era University, Dehradun 248002, IndiaAutomated teller machines (ATM) are widely being used to carry out banking transactions and are becoming one of the necessities of everyday life. ATMs facilitate withdrawal, deposit, and transfer of money from one account to another round the clock. However, this convenience is marred by criminal activities like money snatching and attack on customers, which are increasingly affecting the security of bank customers. In this paper, we propose a video based framework that efficiently identifies abnormal activities happening at the ATM installations and generates an alarm during any untoward incidence. The proposed approach makes use of motion history image (MHI) and Hu moments to extract relevant features from video. Principle component analysis has been used to reduce the dimensionality of features and classification has been carried out by using support vector machine. Analysis has been carried out on different video sequences by varying the window size of MHI. The proposed framework is able to distinguish the normal and abnormal activities like money snatching, harm to the customer by virtue of fight, or attack on the customer with an average accuracy of 95.73%.http://dx.doi.org/10.1155/2015/502737 |
| spellingShingle | Vikas Tripathi Durgaprasad Gangodkar Vivek Latta Ankush Mittal Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM Installations Journal of Electrical and Computer Engineering |
| title | Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM Installations |
| title_full | Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM Installations |
| title_fullStr | Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM Installations |
| title_full_unstemmed | Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM Installations |
| title_short | Robust Abnormal Event Recognition via Motion and Shape Analysis at ATM Installations |
| title_sort | robust abnormal event recognition via motion and shape analysis at atm installations |
| url | http://dx.doi.org/10.1155/2015/502737 |
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