ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER
In computer vision, one of the complex research areas is video surveillance. It is very important to monitor the abnormal events in public places. Due to the technical advances, the usage of cameras is increased for surveillance purpose. As human operators are employed for the observation, their vis...
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| Format: | Article |
| Language: | English |
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XLESCIENCE
2019-12-01
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| Series: | International Journal of Advances in Signal and Image Sciences |
| Subjects: | |
| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/47 |
| _version_ | 1819028415928336384 |
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| author | Manjula Pattnaik |
| author_facet | Manjula Pattnaik |
| author_sort | Manjula Pattnaik |
| collection | DOAJ |
| description | In computer vision, one of the complex research areas is video surveillance. It is very important to monitor the abnormal events in public places. Due to the technical advances, the usage of cameras is increased for surveillance purpose. As human operators are employed for the observation, their visual attention is reduced after long periods. Hence, an automated Abnormal Event Detection (AED) technique is designed in this study. It uses Generalized Autoregressive Conditional Heteroscedasticity (GARCH) which is a statistical model to model the events occurs in the pedestrian pathway. Before modeling, a series of preprocessing steps are employed to detect the moving objects. Multilayer Perceptron (MLP) is used to classify the parameters of GARCH model as normal event or abnormal event. Results show that the events are modeled by GARCH in an efficient manner which provides promising results for AED. |
| first_indexed | 2024-12-21T05:58:01Z |
| format | Article |
| id | doaj.art-3753c9ceb9eb44c0a5a492e80d1e481a |
| institution | Directory Open Access Journal |
| issn | 2457-0370 |
| language | English |
| last_indexed | 2024-12-21T05:58:01Z |
| publishDate | 2019-12-01 |
| publisher | XLESCIENCE |
| record_format | Article |
| series | International Journal of Advances in Signal and Image Sciences |
| spelling | doaj.art-3753c9ceb9eb44c0a5a492e80d1e481a2022-12-21T19:13:48ZengXLESCIENCEInternational Journal of Advances in Signal and Image Sciences2457-03702019-12-0152152210.29284/ijasis.5.2.2019.15-2247ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIERManjula PattnaikIn computer vision, one of the complex research areas is video surveillance. It is very important to monitor the abnormal events in public places. Due to the technical advances, the usage of cameras is increased for surveillance purpose. As human operators are employed for the observation, their visual attention is reduced after long periods. Hence, an automated Abnormal Event Detection (AED) technique is designed in this study. It uses Generalized Autoregressive Conditional Heteroscedasticity (GARCH) which is a statistical model to model the events occurs in the pedestrian pathway. Before modeling, a series of preprocessing steps are employed to detect the moving objects. Multilayer Perceptron (MLP) is used to classify the parameters of GARCH model as normal event or abnormal event. Results show that the events are modeled by GARCH in an efficient manner which provides promising results for AED.https://xlescience.org/index.php/IJASIS/article/view/47abnormal event detection, anomaly detection, garch modeling, multilayer perceptron, neural network. |
| spellingShingle | Manjula Pattnaik ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER International Journal of Advances in Signal and Image Sciences abnormal event detection, anomaly detection, garch modeling, multilayer perceptron, neural network. |
| title | ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER |
| title_full | ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER |
| title_fullStr | ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER |
| title_full_unstemmed | ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER |
| title_short | ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER |
| title_sort | abnormal event detection in pedestrian pathway using garch model and mlp classifier |
| topic | abnormal event detection, anomaly detection, garch modeling, multilayer perceptron, neural network. |
| url | https://xlescience.org/index.php/IJASIS/article/view/47 |
| work_keys_str_mv | AT manjulapattnaik abnormaleventdetectioninpedestrianpathwayusinggarchmodelandmlpclassifier |