Online video-based abnormal detection using highly motion techniques and statistical measures
At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between f...
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Online Access: | http://eprints.utm.my/id/eprint/91355/1/AhlamAlDhamari2019_OnlineVideoBasedAbnormal.pdf http://eprints.utm.my/id/eprint/91355/ http://www.dx.doi.org/10.12928/TELKOMNIKA.v17i4.12753 |
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my.utm.913552021-06-30T12:08:12Z http://eprints.utm.my/id/eprint/91355/ Online video-based abnormal detection using highly motion techniques and statistical measures Al-Dhamari, A. Sudirman, R. Mahmood, N. H. Khamis, N. H. Yahya, A. TK Electrical engineering. Electronics Nuclear engineering At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for the accuracy while attaining simultaneously low values for the processing time. Universitas Ahmad Dahlan 2019 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/91355/1/AhlamAlDhamari2019_OnlineVideoBasedAbnormal.pdf Al-Dhamari, A. and Sudirman, R. and Mahmood, N. H. and Khamis, N. H. and Yahya, A. (2019) Online video-based abnormal detection using highly motion techniques and statistical measures. Telkomnika (Telecommunication Computing Electronics and Control), 17 (4). ISSN 1693-6930 http://www.dx.doi.org/10.12928/TELKOMNIKA.v17i4.12753 DOI: 10.12928/TELKOMNIKA.v17i4.12753 |
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TK Electrical engineering. Electronics Nuclear engineering Al-Dhamari, A. Sudirman, R. Mahmood, N. H. Khamis, N. H. Yahya, A. Online video-based abnormal detection using highly motion techniques and statistical measures |
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At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for the accuracy while attaining simultaneously low values for the processing time. |
format |
Article |
author |
Al-Dhamari, A. Sudirman, R. Mahmood, N. H. Khamis, N. H. Yahya, A. |
author_facet |
Al-Dhamari, A. Sudirman, R. Mahmood, N. H. Khamis, N. H. Yahya, A. |
author_sort |
Al-Dhamari, A. |
title |
Online video-based abnormal detection using highly motion techniques and statistical measures |
title_short |
Online video-based abnormal detection using highly motion techniques and statistical measures |
title_full |
Online video-based abnormal detection using highly motion techniques and statistical measures |
title_fullStr |
Online video-based abnormal detection using highly motion techniques and statistical measures |
title_full_unstemmed |
Online video-based abnormal detection using highly motion techniques and statistical measures |
title_sort |
online video-based abnormal detection using highly motion techniques and statistical measures |
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Universitas Ahmad Dahlan |
publishDate |
2019 |
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http://eprints.utm.my/id/eprint/91355/1/AhlamAlDhamari2019_OnlineVideoBasedAbnormal.pdf http://eprints.utm.my/id/eprint/91355/ http://www.dx.doi.org/10.12928/TELKOMNIKA.v17i4.12753 |
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1705056701759094784 |
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