A self-selective correlation ship tracking method for smart ocean systems
المؤلف | Kang X. |
المؤلف | Song B. |
المؤلف | Guo J. |
المؤلف | Du X. |
المؤلف | Guizani M. |
تاريخ الإتاحة | 2020-04-25T01:02:21Z |
تاريخ النشر | 2019 |
اسم المنشور | Sensors (Switzerland) |
المصدر | Scopus |
الرقم المعياري الدولي للكتاب | 14248220 |
الملخص | In recent years, with the development of the marine industry, the ship navigation environment has become more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count sailing ships to ensure maritime security and facilitate management for Smart Ocean systems. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly includes: (1) A self-selective model with a negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of the classifier at the same time; (2) a bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were over 8 % higher than Discriminative Scale Space Tracking (DSST) on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 frames per second (FPS). |
راعي المشروع | This research was supported by the National Natural Science Foundation of China under Grant (No. 61772387 and No. 61802296), the Fundamental Research Funds of Ministry of Education and China Mobile (MCM20170202), the Fundamental Research Funds for the Central Universities (JB180101), China Postdoctoral Science Foundation Grant (No. 2017M620438), and supported by ISN State Key Laboratory. |
اللغة | en |
الناشر | MDPI AG |
الموضوع | Box regression Correlation filter Negative samples mining Self-selective model Ship tracking |
النوع | Article |
رقم العدد | 4 |
رقم المجلد | 19 |
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