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Detection and Tracking Meet Drones Challenge
Open accessAbstract
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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“It includes 263 video sequences and more than 10000 static images, all of which were collected using various drones in 14 different cities in diverse weather and lighting conditions and at various altitudes.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [40]
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“Another relevant dataset is Visdrone [39], [40], the largest drone-based aerial dataset, which contains four tasks: object detection in images, videos, SOT, and MOT.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [40]
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Metadata
- 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)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-11-01
- Volume
- 44
- Issue
- 11
- Pages
- 7380-7399
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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