Chen, G.Wang, W.He, Z.Wang, L.Yuan, Y.Zhang, D.Zhang, J.Zhu, P.Gool, L. V.Han, J.Hoi, S.Hu, Q.Liu, M.Sciarrone, A.Sun, C.Garibotto, C.Tran, D. N. N.Lavagetto, F.Haleem, H.Motorcu, HakkıAteş, H. F.Jeon, H. J.Bisio, I.Jeon, J. W.Li, J.Pham, J. H.Jeon, M.Feng, Q.Li, S.Tran, T. H. P.Pan, X.Song, Y. M.Yao, Y.Du, Y.Xu, Z.Luo, Z.2023-05-032023-05-0320211550-5499http://hdl.handle.net/10679/8170https://doi.org/10.1109/ICCVW54120.2021.00318Vision 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.enginfo:eu-repo/semantics/restrictedAccessConference paper2839284600073965110210210.1109/ICCVW54120.2021.00318BenchmarkChallengeDroneMulti-object trackingVisDrone2-s2.0-85122776407