Cited by 1,336Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsVisual Attention and Saliency Detection

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Detection and Tracking Meet Drones Challenge

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Zhu, Pengfei, Wen, Longyin, Du, Dawei, Bian, Xiao, Fan, Heng, Hu, Qinghua, Ling, Haibin·2022·IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI 10.1109/tpami.2021.3119563

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Abstract

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset.

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Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
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Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105

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Jin, X., Li, J., Ma, J., & Zhao, Y. (2026) · Mathematics · doi:10.3390/math14111989 · Cited by 1
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Yuan, X., Li, T., Xie, T., & Xiao, N. (2026) · Applied Sciences · doi:10.3390/app16126086 · Cited by 1
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Lv, Y., Jin, C., Liu, S., 文, 冬., & Song, X. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.106100 · Cited by 1
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Zhang, K., & Wang, H. (2026) · Remote Sensing · doi:10.3390/rs18152609 · Cited by 1
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Authors: Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H.
Affiliations: Tianjin University (CN)
JD Finance America Corporation, Mountain View, CA, USA
State University of New York (US)
GE Global Research (United States) (US)
University of North Texas (US)
Stony Brook University (US)
ORCID: Zhu, Pengfei
Wen, Longyin
Du, Dawei
Bian, Xiao
Fan, Heng
Hu, Qinghua
Ling, Haibin
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-11-01
Volume: 44
Issue: 11
Pages: 7380-7399
ISSN: 0162-8828 (ISSN-L)
2160-9292
1939-3539
DOI: 10.1109/tpami.2021.3119563
OpenAlex: W3205100603
Cited by: 1,336
References: 169
Citations by year: 2026: 422 · 2025: 409 · 2024: 257 · 2023: 173 · 2022: 73
FWCI: 45.41
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Key Research and Development Program of China (2019YFB2101904)
National Natural Science Foundation of China (61732011, 61876127, 61925602)
Applied Basic Research Program of Qinghai (2019-ZJ-7017)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords: VisDrone dataset, multi-target tracking, single object tracking, video object detection, object detection, benchmark challenge
MeSH terms: Unmanned Aerial Devices, Algorithms, Photography

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