Real-time detection, tracking and classification of multiple moving objects in UAV videos

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dc.citation.epage950en_US
dc.citation.spage945en_US
dc.contributor.authorBaykara, Hüseyin Canen_US
dc.contributor.authorBıyık, Erdemen_US
dc.contributor.authorGül, Gamzeen_US
dc.contributor.authorOnural, Denizen_US
dc.contributor.authorÖztürk, Ahmet Safaen_US
dc.contributor.authorYıldız, İlkayen_US
dc.coverage.spatialBoston, MA, USA
dc.date.accessioned2019-02-21T16:04:28Z
dc.date.available2019-02-21T16:04:28Z
dc.date.issued2017-11en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 6-8 Nov. 2017
dc.descriptionConference name: IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017
dc.description.abstractUnnamed Aerial Vehicles (UAVs) are becoming increasingly popular and widely used for surveillance and reconnaissance. There are some recent studies regarding moving object detection, tracking, and classification from UAV videos. A unifying study, which also extends the application scope of such previous works and provides real-Time results, is absent from the literature. This paper aims to fill this gap by presenting a framework that can robustly detect, track and classify multiple moving objects in real-Time, using commercially available UAV systems and a common laptop computer. The framework can additionally deliver practical information about the detected objects, such as their coordinates and velocities. The performance of the proposed framework, which surpasses human capabilities for moving object detection, is reported and discussed.
dc.description.provenanceMade available in DSpace on 2019-02-21T16:04:28Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipACKNOWLEDGMENTS We thank Ersin Yar, Bumin Kaan Aydın, Dr. Cem Tekin and Dr. Orhan Arıkan for comments and fruitful discussions throughout the project. This work was supported by HAVELSAN Inc. and Bilkent University, Electrical and Electronics Engineering Department. We acknowledgefunding by TÜB˙TAK B˙DEB 2209-B grant.
dc.identifier.doi10.1109/ICTAI.2017.00145
dc.identifier.urihttp://hdl.handle.net/11693/50188
dc.language.isoEnglish
dc.publisherIEEE
dc.relation.isversionofhttps://doi.org/10.1109/ICTAI.2017.00145
dc.source.titleProceedings - International Conference on Tools with Artificial Intelligence, ICTAI 2017en_US
dc.subjectAerial image classificationen_US
dc.subjectAerial videoen_US
dc.subjectAutomationen_US
dc.subjectDeep learningen_US
dc.subjectLow altitudeen_US
dc.subjectMoving object detectionen_US
dc.subjectObject trackingen_US
dc.subjectReal timeen_US
dc.subjectSurveillanceen_US
dc.subjectTransfer learningen_US
dc.titleReal-time detection, tracking and classification of multiple moving objects in UAV videosen_US
dc.typeConference Paperen_US

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