VisDrone-MOT2021: The vision meets drone multiple object tracking challenge results

Basit Öğe Kaydı
dc.contributor.authorChen, Guanlin
dc.contributor.authorWang, Wenguan
dc.contributor.authorHe, Zhijian
dc.contributor.authorWang, Lujia
dc.contributor.authorYuan, Yixuan
dc.contributor.authorZhang, Dingwen
dc.contributor.authorZhang, Jinglin
dc.contributor.authorZhu, Pengfei
dc.contributor.authorGool, Luc Van
dc.contributor.authorHan, Junwei
dc.contributor.authorHoi, Steven
dc.contributor.authorHu, Qinghua
dc.contributor.authorLiu, Ming
dc.contributor.authorSciarrone, Andrea
dc.contributor.authorSun, Chao
dc.contributor.authorGaribotto, Chiara
dc.contributor.authorTran, Duong Nguyen-Ngoc
dc.contributor.authorLavagetto, Fabio
dc.contributor.authorHaleem, Halar
dc.contributor.authorMotorcu, Hakkı
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorNguyen, Huy Hung
dc.contributor.authorJeon, Hyung Joon
dc.contributor.authorBisio, Igor
dc.contributor.authorJeon, Jae Wook
dc.contributor.authorLi, Jiahao
dc.contributor.authorPham, Long Hoang
dc.contributor.authorJeon, Moongu
dc.contributor.authorFeng, Qianyu
dc.contributor.authorLi, Shengwen
dc.contributor.authorTran, Tai Huu-Phuong
dc.contributor.authorPan, Xiao
dc.contributor.authorSong, Young-min
dc.contributor.authorYao, Yuehan
dc.contributor.authorDu, Yunhao
dc.contributor.authorXu, Zhenyu
dc.contributor.authorLuo, Zhipeng
dc.date.accessioned2022-01-31T13:12:37Z
dc.date.available2022-01-31T13:12:37Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractVision Meets Drone: Multiple Object Tracking (VisDrone-MOT2021) challenge - the forth annual activity organized by the VisDrone team - focuses on benchmarking UAV MOT algorithms in realistic challenging environments. It is held in conjunction with ICCV 2021. VisDrone-MOT2021 contains 96 video sequences in total, including 56 sequences (~24K frames) for training, 7 sequences (~3K frames) for validation and 33 sequences (~13K frames) for testing. Bounding-box annotations for novel object categories are provided every frame and temporally consistent instance IDs are also given. Additionally, occlusion ratio and truncation ratio are provided as extra useful annotations. The results of eight state-of-the-art MOT algorithms are reported and discussed. We hope that our VisDrone-MOT2021 challenge will facilitate future research and applications in the field of UAV vision. The website of our challenge can be found at http://www.aiskyeye.com/.
dc.identifier.citationChen, G., Wang, W., He, Z., Wang, L., Yuan, Y., Zhang, D. ... Luo, Z. (2021). VisDrone-MOT2021: The vision meets drone multiple object tracking challenge results. IEEE/CVF International Conference on Computer Vision (ICCVW) içinde (2839-2846. ss.). Virtual, 27-30 September 2021. https://dx.doi.org/10.1109/ICCVW54120.2021.00318
dc.identifier.doi10.1109/ICCVW54120.2021.00318
dc.identifier.endpage2846
dc.identifier.isbn9781665401913
dc.identifier.issn1550-5499
dc.identifier.scopusqualityN/A
dc.identifier.startpage2839
dc.identifier.urihttps://dx.doi.org/10.1109/ICCVW54120.2021.00318
dc.identifier.urihttps://hdl.handle.net/20.500.12511/8947
dc.identifier.volume2021
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE/CVF International Conference on Computer Vision (ICCVW)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectBenchmark
dc.subjectChallenge
dc.subjectDrone
dc.subjectMulti-Object Tracking
dc.subjectVisDrone
dc.titleVisDrone-MOT2021: The vision meets drone multiple object tracking challenge results
dc.typeConference Object

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Ateş-Hasan Fehmi-2021.pdf
Boyut:
389.84 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bildiri Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu