Segmentation-Guided Optical Flow Integration for Ship Tracking in Dynamic Waters

Segmentation-Guided Optical Flow Integration for Ship Tracking in Dynamic Waters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Segmentation-Guided Optical Flow Integration for Ship Tracking in Dynamic Waters Mahmoud Alsaleh, Burhan Pektaş This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8490162/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Multi-object ship tracking in maritime environments remains challenging due to dynamic water surfaces, camera drift, and low target visibility. Conventional tracking-by-detection pipelines—such as YOLO combined with DeepSORT—often degrade under these conditions, leading to fragmented trajectories and unstable identity association. In this work, we propose a segmentation-guided, flow-aware tracking framework that enhances YOLOv11-based detection and segmentation with motion cues derived from optical flow. The proposed method has three components: (1) segmentation-guided sparse KLT flow, extracting motion only within ship masks to suppress wave/background clutter; (2) camera-motion compensation via median background-flow subtraction; and (3) a flow-aware association cost inside DeepSORT that fuses appearance similarity with motion consistency (with reliability-gated weighting). Together, these additions improve temporal stability without modifying the tracker’s underlying state model. We evaluate the approach on a curated set of maritime videos spanning diverse conditions, including heavy traffic, fog, night scenes, and empty-water cases. Experimental results based on stability-oriented proxy metrics and per-video MOT analyses demonstrate that the proposed method substantially reduces track fragmentation and improves visual consistency compared to the legacy DeepSORT baseline, with additional MOTA gains observed in challenging crowded and camera-motion scenarios, at a moderate computational overhead. These findings highlight the effectiveness of segmentation-guided motion cues for robust ship tracking in real-world maritime surveillance scenarios. Multi-object tracking Computer vision Optical flow Ship tracking Maritime surveillance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8490162","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584320712,"identity":"cf5820d7-08d3-4771-b167-514fe156bbfd","order_by":0,"name":"Mahmoud Alsaleh","email":"data:image/png;base64,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","orcid":"","institution":"Üsküdar University","correspondingAuthor":true,"prefix":"","firstName":"Mahmoud","middleName":"","lastName":"Alsaleh","suffix":""},{"id":584320713,"identity":"a35e17ff-f576-4832-828a-19210a933529","order_by":1,"name":"Burhan Pektaş","email":"","orcid":"","institution":"Üsküdar University","correspondingAuthor":false,"prefix":"","firstName":"Burhan","middleName":"","lastName":"Pektaş","suffix":""}],"badges":[],"createdAt":"2025-12-31 13:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8490162/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8490162/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101754062,"identity":"0a766f9c-75f2-4e23-bd9c-c5f803c13dbd","added_by":"auto","created_at":"2026-02-03 10:41:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3906235,"visible":true,"origin":"","legend":"","description":"","filename":"SegmentationGuidedOpticalFlowIntegrationforShipTrackinginDynamicWaters.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8490162/v1_covered_513cf4a6-a22d-432b-8e78-df6efe26455f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Segmentation-Guided Optical Flow Integration for Ship Tracking in Dynamic Waters","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multi-object tracking, Computer vision, Optical flow, Ship tracking, Maritime surveillance","lastPublishedDoi":"10.21203/rs.3.rs-8490162/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8490162/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Multi-object ship tracking in maritime environments remains challenging due to dynamic water surfaces, camera drift, and low target visibility. Conventional tracking-by-detection pipelines—such as YOLO combined with DeepSORT—often degrade under these conditions, leading to fragmented trajectories and unstable identity association. In this work, we propose a segmentation-guided, flow-aware tracking framework that enhances YOLOv11-based detection and segmentation with motion cues derived from optical flow.\nThe proposed method has three components: (1) segmentation-guided sparse KLT flow, extracting motion only within ship masks to suppress wave/background clutter; (2) camera-motion compensation via median background-flow subtraction; and (3) a flow-aware association cost inside DeepSORT that fuses appearance similarity with motion consistency (with reliability-gated weighting). 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