YOLOv8 ile gerçek zamanlı çoklu nesne takibi

Name: YOLOv8 ile gerçek zamanlı çoklu nesne takibi
Author: Ates, Hasan Fehmi, Celik, Cansu, Adak, Berk, Adak, Mert, Berk, Durmus

İsim	YOLOv8 ile gerçek zamanlı çoklu nesne takibi
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

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YOLOv8 ile gerçek zamanlı çoklu nesne takibi

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