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VisDrone2019-DET Dataset

1020913882
Tagsurveillance
Taskobject detection
Release YearMade in 2019
Licensepublicly available
Download3 GB

Introduction #

Pengfei Zhu, Longyin Wen, Dawei Duet al.

The authors have made available the VisDrone2019-DET Dataset, a comprehensive collection of drone-captured images tailored for object detection tasks. Spanning various urban and suburban locales across 14 different cities in China, from north to south, the VisDrone dataset stands as the most extensive of its kind ever published. This dataset facilitates thorough evaluation and exploration of visual analysis algorithms specifically designed for drone platforms.

Motivation

In recent years, drones (or UAVs) equipped with cameras have garnered significant attention. It is projected that the global commercial drone market size will reach $501.4 billion by 2028, with a compound annual growth rate of 57.5% from 2021 to 2028. Utilizing embedded devices, drones have the capability to analyze captured data and give rise to a multitude of new application scenarios:

  • Agriculture. Drones can provide valuable insights to help farmers or ranchers optimize agriculture operations, monitor crop growth and keep herds safe, etc.
  • Aerial photography. Drones are used to extract aerial photography images instead of expensive cranes and helicopters.
  • Shipping and delivery. Drones can efficiently send packages such as medical supplies, food, or other goods to the designated places.
  • Security and surveillance. Drones can provide realtime visibility into security threats and emergency situations by monitoring large regions.
  • Search and rescue. Drones are useful to help search missing persons, fugitives, or rescue survivors and drop supplies in difficult terrains and harsh conditions.

As a result, there is a growing demand for the automatic interpretation of visual data gathered from drones, drawing computer vision technology closer to these aerial platforms. Object detection and object tracking, being two fundamental challenges in computer vision, are currently undergoing extensive research and development. However, despite significant advancements in various application domains such as internet and security surveillance, existing methods are often suboptimal when applied to sequences or images captured by drones. Notably, drone-captured video sequences present several unique challenges not typically encountered in traditional computer vision datasets:

  • Viewpoint variations: comparing to surveillance cameras with fixed viewpoints, drone-equipped cameras monitor the objects in arbitrary viewpoints.
  • Scale variations: drone-equipped cameras monitor the objects at different altitudes, resulting in large variations of scales of objects.
  • Motion blur: videos are generally recorded by the droneequipped cameras in the moving process, bringing in considerable motion blurs of the recorded videos.

Hence, there is a critical need to advance the development and assessment of vision algorithms tailored for visual data captured by drones. However, progress toward this objective is significantly hindered by the absence of publicly available large-scale benchmarks or datasets. While some recent endeavors have been dedicated to constructing datasets captured by drones, with a particular focus on object detection or tracking, these datasets remain constrained in size and scope due to the challenges associated with data collection and annotation. Consequently, thorough evaluations of existing or newly developed algorithms remain an ongoing challenge. There is a pressing need for a more inclusive and comprehensive benchmark to further advance research in this field.

Dataset description

A foundational element crucial for the effective evaluation of algorithms is the availability of a comprehensive dataset. In this regard, the authors of VisDrone have meticulously curated the most extensive dataset to date, aiming to propel research in object detection and tracking on drones forward. This dataset comprises 263 video clips encompassing 179,264 frames, along with an additional 10,209 static images. These videos and images were captured using various drone platforms, including the DJI Mavic and Phantom series (3, 3A, 3SE, 3P, 4, 4A, 4P), across a diverse range of scenarios spanning 14 different cities in China. These cities include Tianjin, Hongkong, Daqing, Ganzhou, Guangzhou, Jincang, Liuzhou, Nanjing, Shaoxing, Shenyang, Nanyang, Zhangjiakou, Suzhou, and Xuzhou. The dataset encompasses a wide array of weather and lighting conditions, thus representing a myriad of scenarios encountered in daily life. The video clips boast a maximal resolution of 3840 Γ— 2160, while the static images reach a resolution of 2000 Γ— 1500, ensuring high-quality data for research purposes. The VisDrone benchmark focuses on the following four tasks: Object Detection in Images (DET), Object Detection in Videos (VID), Single-Object Tracking (SOT) and Multi-Object Tracking (MOT).

image

Annotated example images in the proposed datasets. The dashed bounding box indicates the object is occluded. Different colors indicate different classes of objects. For better visualization, only a few attributes are displayed.

The VisDrone-DET dataset tackles the problem of localizing multiple object categories in the image. For each image, algorithms are required to predict the bounding boxes of all the object instances of predefined object categories, with a real valued confidence. The dataset consists of 10, 209 images in unconstrained challenging scenes, including 6, 471 images in the training subset, 548 in the validation subset, 1, 580 in the test-challenge subset, and 1, 610 in the test-dev subset. Notably, the class imbalance issue significantly affect the detection performance. For example, the number of the awning-tricycle instances is more than 40Γ— less than the car instances.

The authors mainly focus on people and vehicles in daily life and define ten object categories, including pedestrian, person(if a human maintains standing pose or walking, they classify it as pedestrian; otherwise, it is classified as a person), car, van, bus, truck, motor, bicycle, awning tricycle, and tricycle. Some rarely occurring vehicles are ignored, e.g., machineshop truck, forklift truck, and tanker. The authors also provide two attributes of each annotated bounding box to analyze the algorithms thoroughly, i.e., the occlusion* and truncation ratios. Specifically, occlusion ratio α denotes the fraction of objects being occluded by other objects or background, including no occlusion (δ = 0%), partial occlusion (δ ∈ (0%, 50%]), and heavy occlusion (δ ∈ (50%, 100%]). The truncation ratio denotes the degree of object parts appearing outside a frame when the object is captured near the frame boundary. It is estimated based on the region outside the frame by the human.

image

The number of objects with different occlusion degrees of different object categories in the subsets of the dataset.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

VisDrone2019-DET Dataset is a dataset for an object detection task. It is used in the surveillance domain.

The dataset consists of 10209 images with 471266 labeled objects belonging to 13 different classes including car, pedestrian, van, and other: motor, person, truck, ignored region, bicycle, bus, tricycle, awning tricycle, other, and people.

Images in the VisDrone2019-DET dataset have bounding box annotations. There are 1580 (15% of the total) unlabeled images (i.e. without annotations). There are 4 splits in the dataset: train (6471 images), test dev (1610 images), test challenge (1580 images), and val (548 images). Alternatively, the dataset could be split into 3 occlusions: no occlusion (239630 instances), partial occlusion (187728 instances), and heavy occlusion (43908 instances), or into 2 truncations: no truncation (453044 instances) and partial truncation (18222 instances). The dataset was released in 2019 by the Tianjin University, China, Finance America Corporation, USA, University at Albany, USA, GE Global Research, USA, University of North Texas, USA, and Stony Brook University, USA.

Dataset Poster

Explore #

VisDrone2019-DET dataset has 10209 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
OpenSample annotation mask from VisDrone2019-DETSample image from VisDrone2019-DET
πŸ‘€
Have a look at 10209 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 13 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 13
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
carβž”
rectangle
8178
187005
22.87
4.46%
pedestrianβž”
rectangle
7083
109187
15.42
0.7%
vanβž”
rectangle
6537
32702
5
1.13%
motorβž”
rectangle
5516
40378
7.32
0.45%
truckβž”
rectangle
4567
16284
3.57
1.53%
personβž”
rectangle
4429
32184
7.27
0.29%
ignored regionβž”
rectangle
3521
12371
3.51
0.95%
bicycleβž”
rectangle
3496
13069
3.74
0.25%
busβž”
rectangle
2992
9117
3.05
1.52%
tricycleβž”
rectangle
2270
6387
2.81
0.47%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 13
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
car
rectangle
187005
0.21%
34.22%
0%
1px
0.1%
791px
80.52%
41px
4.2%
3px
0.2%
718px
42.5%
pedestrian
rectangle
109187
0.05%
5.62%
0%
3px
0.29%
385px
50.33%
31px
3.33%
3px
0.15%
274px
16.36%
motor
rectangle
40378
0.06%
5.69%
0%
2px
0.33%
281px
30.98%
28px
2.91%
3px
0.15%
260px
18.38%
van
rectangle
32702
0.23%
13.34%
0%
4px
0.38%
545px
48.63%
46px
4.77%
4px
0.25%
636px
31.8%
person
rectangle
32184
0.04%
4.98%
0%
2px
0.19%
339px
32.29%
26px
2.78%
3px
0.15%
216px
15.62%
truck
rectangle
16284
0.47%
30.55%
0%
4px
0.38%
705px
71.5%
61px
6.58%
4px
0.29%
984px
61.1%
bicycle
rectangle
13069
0.07%
2.52%
0%
2px
0.19%
272px
31.76%
31px
3.02%
4px
0.2%
333px
16.65%
ignored region
rectangle
12371
0.27%
14.1%
0%
1px
0.07%
554px
71.67%
40px
4.39%
3px
0.2%
1425px
71.25%
bus
rectangle
9117
0.52%
22.14%
0%
4px
0.38%
664px
54.86%
67px
7.85%
4px
0.29%
691px
49.36%
tricycle
rectangle
6387
0.17%
4.88%
0%
4px
0.33%
348px
29.59%
40px
4.41%
3px
0.2%
412px
20.6%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 96974 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 96974
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
26px
2.48%
26px
1.86%
0.05%
2βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
25px
2.38%
24px
1.71%
0.04%
3βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
24px
2.29%
25px
1.79%
0.04%
4βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
24px
2.29%
18px
1.29%
0.03%
5βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
29px
2.76%
22px
1.57%
0.04%
6βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
29px
2.76%
17px
1.21%
0.03%
7βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
26px
2.48%
16px
1.14%
0.03%
8βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
21px
2%
19px
1.36%
0.03%
9βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
25px
2.38%
20px
1.43%
0.03%
10βž”
pedestrian
rectangle
9999987_00000_d_0000005.jpg
1050 x 1400
25px
2.38%
21px
1.5%
0.04%

License #

The VisDrone2019-DET Dataset is publicly available.

Source

Citation #

If you make use of the VisDrone-DET data, please cite the following reference:

@article{zhu2021detection,
  title={Detection and tracking meet drones challenge},
  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={44},
  number={11},
  pages={7380--7399},
  year={2021},
  publisher={IEEE}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-vis-drone-2019-det-dataset,
  title = { Visualization Tools for VisDrone2019-DET Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/vis-drone-2019-det } },
  url = { https://datasetninja.com/vis-drone-2019-det },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset VisDrone2019-DET can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='VisDrone2019-DET', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

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