Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5500
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dc.contributor.authorTeket, O. M.-
dc.contributor.authorYetik, İmam Şamil-
dc.date.accessioned2021-09-11T15:19:08Z-
dc.date.available2021-09-11T15:19:08Z-
dc.date.issued2020en_US
dc.identifier.citation4th International Conference on Vision, Image and Signal Processing, ICVISP 2020, 9 December 2020 through 11 December 2020, , 167661en_US
dc.identifier.isbn9781450389532-
dc.identifier.urihttps://doi.org/10.1145/3448823.3448882-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/5500-
dc.description.abstractThe aim of this study is to obtain an algorithm to analyze a basketball training in real time. That is, the approach should be able to detect the correct players and scores for each player. For this purpose, we first propose a method to detect the shots and if they are makes or misses. Specifically, we use a deep learning model to detect the position of the hoop and background subtraction to detect the shot. Then, scoring is determined by another neural network using classification. After the determination of the shot, we go back in our buffered images to find the player who sent the shot. The player is detected by YOLOv3-tiny and identified by a deep unsupervised reidentification model. To keep the real-time aim, all models employ mobile networks as base with different parameters and training methods. The experiments were conducted on two training videos. The experiment results show the effectiveness of the current method with over 95% accuracy on scoring identification and up to 91.4% overall accuracy on reidentification of 4 players in real-time. © 2020 ACM.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofACM International Conference Proceeding Seriesen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectRe-identificationen_US
dc.subjectVideo analysisen_US
dc.titleA Fast Deep Learning Based Approach for Basketball Video Analysisen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.scopus2-s2.0-85102950958en_US
dc.institutionauthorYetik, Imam Şamil-
dc.identifier.doi10.1145/3448823.3448882-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference4th International Conference on Vision, Image and Signal Processing, ICVISP 2020en_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.5. Department of Electrical and Electronics Engineering-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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