NF-UAVTrack

NF-UAVTrack (Night-and-Fog UAV Tracking) is a visible-light anti-UAV dataset for single-object tracking and detection of small UAVs. All videos were filmed from the ground with smartphones at night in fog, and the flying UAV target is annotated frame by frame in YOLO format.

• 25 videos, 12,127 frames, about 1.2 GB
• Four scene types (open grassland, sports field, park, between buildings) and two recording periods (April 2025 and April 2026)
• 10,936 frames with exactly one target box and 1,191 target-absent frames (empty label files)
• Resolutions: 3840×2160 (video01–video09) and 1920×1080 (video10–video25)
• Small targets: the mean box covers 2.67 % × 3.96 % of the image width × height, and the smallest box covers 0.75 % × 1.06 %
• The target leaves the view or becomes invisible in 20 of the 25 videos, in 108 separate absence intervals

Collection

All videos were recorded by the authors from the ground with two smartphones (Xiaomi 12S and iPhone 15 Pro) in four different areas of the Future Campus of Soochow University, Suzhou, Jiangsu, China, one per scene type (open grassland, sports field, park and between buildings), in two recording periods: April 2025 and April 2026. Recording took place in foggy conditions between 23:00 and 01:00 the following day (China Standard Time, UTC+8). The targets are a YH F450 and a DJI Mavic 4 in flight.

Frames were extracted from the videos, and the target was annotated manually, frame by frame, with an axis-aligned bounding box. Two annotators labelled separate sets of videos and reviewed each other's annotations. Each sequence video01 … video25 is one continuous recording of one target, with the original frame order preserved.

Dataset structure

NF-UAVTrack/
├── README.md
├── LICENSE
├── metadata.csv
├── images/
│   ├── video01/
│   │   ├── frame-000001.jpg
│   │   └── ...
│   └── ...            # video01 … video25
└── labels/
    ├── video01/
    │   ├── frame-000001.txt
    │   └── ...        # same basename as the corresponding image
    └── ...

Every image has exactly one label file with the same basename, and every label file has an image. There are no orphan files.

metadata.csv lists every frame:

column	meaning
video	sequence id, e.g. video01
frame	frame id, e.g. frame-000001
image_path	image path relative to the repository root
label_path	label path relative to the repository root
has_target	1 if the frame has a target box, 0 if the frame is target-absent

Label format

Labels use the standard YOLO detection format: one text file per frame, with coordinates normalised to [0, 1] by the image width and height.

class_id  x_center  y_center  width  height

Example (labels/video01/frame-000001.txt):

0 0.444961 0.817969 0.031459 0.040373

• There is a single class, class_id = 0, the UAV target.
• Each annotated frame has exactly one box.
• Empty label files are intentional. A 0-byte .txt file marks a target-absent frame: the target has left the field of view or is not visible in the fog. Keep these files, because trackers are evaluated on whether they handle the target's disappearance and reappearance.

Official split

The accompanying paper uses a fixed split by video:

• Train (14 videos): video01, video03, video04, video05, video06, video07, video10, video11, video12, video15, video17, video18, video21, video22
• Test (11 videos): video02, video08, video09, video13, video14, video16, video19, video20, video23, video24, video25

Split files: https://github.com/windowFlowers/NF-UAVTrack/tree/main/splits

Split by video, not by frame. Neighbouring frames of one video are nearly identical, so a random frame-level split leaks test data into training.

Statistics

Video	Resolution	Frames	With target	Target-absent
video01	3840×2160	583	333	250
video02	3840×2160	775	775	0
video03	3840×2160	2488	2488	0
video04	3840×2160	443	401	42
video05	3840×2160	296	233	63
video06	3840×2160	335	104	231
video07	3840×2160	566	557	9
video08	3840×2160	157	140	17
video09	3840×2160	520	477	43
video10	1920×1080	300	299	1
video11	1920×1080	300	299	1
video12	1920×1080	300	288	12
video13	1920×1080	300	300	0
video14	1920×1080	499	374	125
video15	1920×1080	257	257	0
video16	1920×1080	375	234	141
video17	1920×1080	751	595	156
video18	1920×1080	193	186	7
video19	1920×1080	168	138	30
video20	1920×1080	814	795	19
video21	1920×1080	319	310	9
video22	1920×1080	295	295	0
video23	1920×1080	149	126	23
video24	1920×1080	261	254	7
video25	1920×1080	683	678	5
Total		12,127	10,936	1,191

Box size (normalised by image width / height):

	min	mean	max
width	0.0075	0.0267	0.1139
height	0.0106	0.0396	0.1557

Intended use

• Evaluating and training single-object trackers for anti-UAV surveillance at night and in fog, including target disappearance and reappearance.
• Training and evaluating small-UAV detectors on ground-to-air images taken in low light.

Limitations

• One class and one target per video; not suited to multi-class or multi-object benchmarks.
• Night and fog only; daytime and clear-weather scenes are not covered.
• Two resolutions (3840×2160 and 1920×1080) are mixed. Resize consistently or report results per resolution group.
• Target-absent frames are unevenly distributed (0 % to 69 % of a video), so per-video results can vary widely.

Loading

from datasets import load_dataset

# frame list with paths and has_target flags
ds = load_dataset("windowflower/NF-UAVTrack", data_files="metadata.csv", split="train")
print(ds[0])

# download a single image
from huggingface_hub import hf_hub_download
path = hf_hub_download("windowflower/NF-UAVTrack",
                       "images/video01/frame-000001.jpg", repo_type="dataset")

Note that split="train" above only names the loaded table; it is not the official training split. Use the split files listed above.

To download everything:

git lfs install
git clone https://huggingface.co/datasets/windowflower/NF-UAVTrack

License

Released under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. See LICENSE. You may share and adapt the data, including commercially, as long as you give appropriate credit and indicate any changes.

Citation

If you use this dataset, please cite:

@misc{cai_nfuavtrack_2026,
  title        = {NF-UAVTrack: A Night-and-Fog Visible-Light Anti-UAV Tracking Dataset},
  author       = {Cai, Zhexuan},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/windowflower/NF-UAVTrack}},
  note         = {Source repository: \url{https://github.com/windowFlowers/NF-UAVTrack}. Licensed under CC BY 4.0}
}

The accompanying paper is under review; its reference will be added here once it is published.

The GitHub repository https://github.com/windowFlowers/NF-UAVTrack hosts the official split files and the annotation documentation.

Contact

For questions, corrections or annotation issues, please open a discussion on this repository.