2021
DOI: 10.1016/j.cviu.2020.103101
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Comprehensive comparative evaluation of background subtraction algorithms in open sea environments

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Cited by 14 publications
(9 citation statements)
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References 26 publications
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“…Moreira et al [9] introduced several maritime vessel foreground segmentation methods. Chan [8] evaluated 37 maritime background subtraction algorithms using an established dataset. Prasad et al [2] performed a comprehensive review and evaluation of background subtraction methods based on object detection in a maritime environment.…”
Section: Contents Of Previous Researchmentioning
confidence: 99%
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“…Moreira et al [9] introduced several maritime vessel foreground segmentation methods. Chan [8] evaluated 37 maritime background subtraction algorithms using an established dataset. Prasad et al [2] performed a comprehensive review and evaluation of background subtraction methods based on object detection in a maritime environment.…”
Section: Contents Of Previous Researchmentioning
confidence: 99%
“…Existing investigations of sea-surface object detection using EO sensors have various objectives, with relatively few studies reporting the comprehensive collection and evaluation of sea-surface object-detection methods. Chan [8] investigated different sea-surface object-detection algorithms primarily to improve the performance of maritime background subtraction in the object-detection step. Schöller et al [10] used three different deep learning methods to evaluate the algorithm's detection performance and classification efficiency.…”
Section: Objectives Of Previous Researchmentioning
confidence: 99%
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“…Chan et al [128] compared thirty-seven nonstatic electrooptical sensor (combine visible-light and infrared cameras)-based background subtraction methods; the results indicate that background subtraction algorithms of the multiple features category can better handle maritime challenges, thereby realizing higher accuracy when analyzing visible-light and infrared cameras.…”
Section: Background Subtractionmentioning
confidence: 99%
“…This assumption is violated in many situations like in presence of fog and glitter and for objects that are visually similar to water, leading to failure. Classical background subtraction methods are also not suitable for USVs since the undulating sea continuously rocks the USV and violates the static camera assumption, which causes a high false positive rate [1], [35].…”
Section: A Maritime Detection and Segmentationmentioning
confidence: 99%