HMRN: heat map regression network to detect and track small objects in wide-area motion imagery

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dc.authorid0000-0002-6842-1528
dc.authorid0000-0003-0779-9620
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorSiddique, Arslan
dc.contributor.authorGüntürk, Bahadır
dc.date.accessioned2023-09-25T08:24:53Z
dc.date.available2023-09-25T08:24:53Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractWe propose HMRN, a deep heat map regression network to detect and track small moving objects in wide-area motion imagery (WAMI) by modifying a deep multi-object tracker. Object detection in WAMI images is challenging, because they cover large geographical areas and contain many small vehicles that do not have sufficient appearance-based cues for effective detection. Typically, background subtraction is applied to detect changed regions in WAMI image sequences. However, these methods suffer from high number of false detections. In this paper, we represent objects in WAMI images as heat maps and develop a deep regression network that predicts the object heat maps from current image, previous image and the predicted heat map of the previous image. Experiments are performed on Wright–Patterson Air Force Base (WPAFB) 2009 dataset and results show that the proposed method is almost ten times faster than its competitors while achieving state-of-the-art detection and tracking accuracy as well. We achieve significant reduction in false positives leading to an increase in average precision and F1 scores.
dc.identifier.citationAteş, H. F., Siddique, A. ve Güntürk, B. (2023). HMRN: heat map regression network to detect and track small objects in wide-area motion imagery. Signal, Image and Video Processing, 17(1), 39-45. https://doi.org/10.1007/s11760-022-02201-7
dc.identifier.doi10.1007/s11760-022-02201-7
dc.identifier.endpage45
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85127544727
dc.identifier.scopusqualityQ2
dc.identifier.startpage39
dc.identifier.urihttps://doi.org/10.1007/s11760-022-02201-7
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11491
dc.identifier.volume17
dc.identifier.wos000777057300003en_US
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAteş, Hasan Fehmi
dc.institutionauthorSiddique, Arslan
dc.institutionauthorGüntürk, Bahadır
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofSignal, Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/118E891
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectObject Detection and Tracking
dc.subjectWide-Area Motion Imagery
dc.subjectHeat Map Regression
dc.subjectDeep Learning
dc.titleHMRN: heat map regression network to detect and track small objects in wide-area motion imagery
dc.typeArticle

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