Отрывок: Each frame has a separate ground truth. Four techniques of background subtraction are compared. Precision, sensitivity, false positive rate, specificity and accuracy are the parameters used for evaluation of the proposed techniques. The 2014 DATASET offers a variety of realistic cam- era images (no CGI) and various videos [31]. They have been selected to address a w...
Полная запись метаданных
Поле DC | Значение | Язык |
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dc.contributor.author | Jeevith, S.H. | - |
dc.contributor.author | Lakshmikanth, S. | - |
dc.date.accessioned | 2023-12-29 12:56:12 | - |
dc.date.available | 2023-12-29 12:56:12 | - |
dc.date.issued | 2022-10 | - |
dc.identifier | Dspace\SGAU\20231223\107662 | ru |
dc.identifier.citation | Jeevith SH, Lakshmikanth S. Robust hybrid technique for moving object detection and tracking using cartoon features and fast PCP. Computer Optics 2022; 46(5): 783-789. DOI: 10.18287/2412-6179-CO-1056. | ru |
dc.identifier.uri | https://dx.doi.org/10.18287/2412-6179-CO-1056 | - |
dc.identifier.uri | http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Robust-hybrid-technique-for-moving-object-detection-and-tracking-using-cartoon-features-and-fast-PCP-107662 | - |
dc.description.abstract | In various computer vision applications, the moving object detection is an essential step. Principal Component Analysis (PCA) techniques are often used for this purpose. However, the performance of this method is degraded by camera shake, hidden moving objects, dynamic background scenes, and / or fluctuating exposure. Robust Principal Component Analysis (RPCA) is a useful approach for reducing stationary background noise as it can recover low rank matrices. That is, moving object is formed by the low power models and the static background of RPCA. This paper proposes a simple alternative minimization algorithm to fix minor discrepancies in the original Principal Component Pursuit (PCP) or RPCA function. A novel hybrid method of cartoon texture features used as a data matrix for RPCA taking into account low-ranking and rare matrix is presented. A new non-convex function is proposed to better control the low-range properties of the video background. Simulation results demonstrate that the proposed algorithm is capable of giving consistent random estimates and can indeed improve the accuracy of object recognition in comparison with existing methods. | ru |
dc.language.iso | en | ru |
dc.publisher | Самарский национальный исследовательский университет | ru |
dc.relation.ispartofseries | 46;5 | - |
dc.subject | principal component pursuit | ru |
dc.subject | robust principal component analysis | ru |
dc.subject | cartoon features | ru |
dc.subject | local binary patterns | ru |
dc.title | Robust hybrid technique for moving object detection and tracking using cartoon features and fast PCP | ru |
dc.type | Article | ru |
dc.textpart | Each frame has a separate ground truth. Four techniques of background subtraction are compared. Precision, sensitivity, false positive rate, specificity and accuracy are the parameters used for evaluation of the proposed techniques. The 2014 DATASET offers a variety of realistic cam- era images (no CGI) and various videos [31]. They have been selected to address a w... | - |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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2412-6179_2022_46_5_783-789.pdf | 902.49 kB | Adobe PDF | Просмотреть/Открыть |
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