Yazar
Ates, Hasan Fehmi, Celik, Cansu, Adak, Berk, Adak, Mert, Berk, Durmus
Basım Tarihi
2024-01-01
Basım Yeri
-
IEEE
Konu
TrackEval, UAVDT, Visdrone, Deep learning, YOLOv8, Object tracker
Tür
Belge
Dil
Türkçe
Dijital
Evet
Yazma
Hayır
Kütüphane
Özyeğin Üniversitesi
Demirbaş Numarası
979-8-3503-8897-8
Kayıt Numarası
5aa439b7-b977-4b67-98ee-b3dd6d5bf52e
Lokasyon
Computer Science
Tarih
2024-01-01
Örnek Metin
In this paper, we used advanced deep multi-object trackers for real-time object tracking and models trained with various datasets in YOLOv8 environment. From a wide range of currently developed trackers, the trackers with the best tracking capabilities are used for evaluation in this paper. These are SMILETrack, ByteTrack and BoTSort. Since these object trackers perform tracking via object detection, object detection with YOLOv8, which has passed many performance audits, was used with these trackers to improve tracking performance. The aim of this paper is to obtain a detection model that can accurately detect objects and track at least 15 frames per second in real-time using the trackers described above. In this paper, it is shown that the trained model is able to track 10 different classes of small objects in aerial videos with high accuracy without interruption.
DOI
10.1109/SIU61531.2024.10600933
