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AuthorDu D.
AuthorWen L.
AuthorZhu P.
AuthorFan H.
AuthorHu Q.
AuthorLing H.
AuthorShah M.
AuthorPan J.
AuthorAl-Ali A.
AuthorMohamed A.
AuthorImene B.
AuthorDong B.
AuthorZhang B.
AuthorNesma B.H.
AuthorXu C.
AuthorDuan C.
AuthorCastiello C.
AuthorMencar C.
AuthorLiang D.
AuthorKruger F.
AuthorVessio G.
AuthorCastellano G.
AuthorWang J.
AuthorGao J.
AuthorAbualsaud K.
AuthorDing L.
AuthorZhao L.
AuthorCianciotta M.
AuthorSaqib M.
AuthorAlmaadeed N.
AuthorElharrouss O.
AuthorLyu P.
AuthorWang Q.
AuthorLiu S.
AuthorQiu S.
AuthorPan S.
AuthorAl-Maadeed S.
AuthorKhan S.D.
AuthorKhattab T.
AuthorHan T.
AuthorGolda T.
AuthorXu W.
AuthorBai X.
AuthorXu X.
AuthorLi X.
AuthorZhao Y.
AuthorTian Y.
AuthorLin Y.
AuthorXu Y.
AuthorYao Y.
AuthorXu Z.
AuthorZhao Z.
AuthorLuo Z.
AuthorWei Z.
AuthorZhao Z.
Available date2022-04-21T08:58:26Z
Publication Date2020
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-3-030-66823-5_41
URIhttp://hdl.handle.net/10576/30105
AbstractCrowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algorithms focusing on crowd counting on the drone-captured data due to the lack of comprehensive datasets. To this end, we collect a large-scale dataset and organize the Vision Meets Drone Crowd Counting Challenge (VisDrone-CC2020) in conjunction with the 16th European Conference on Computer Vision (ECCV 2020) to promote the developments in the related fields. The collected dataset is formed by 3,360 images, including 2,460 images for training, and 900 images for testing. Specifically, we manually annotate persons with points in each video frame. There are 14 algorithms from 15 institutes submitted to the VisDrone-CC2020 Challenge. We provide a detailed analysis of the evaluation results and conclude the challenge. More information can be found at the website: http://www.aiskyeye.com/. 2020, Springer Nature Switzerland AG.
SponsorNational Natural Science Foundation of China;Natural Science Foundation of Tianjin City
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectDrones
Large dataset
Statistical tests
Background clutter
Evaluation results
Large-scale dataset
Small objects
Video frame
Computer vision
TitleVisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
TypeConference Paper
Pagination675-691
Volume Number12538 LNCS


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