Long-Duration Drone Tracking Dataset

Associated Paper: Detector-Augmented SAMURAI for Long-Duration Drone Tracking

Published in the Proceedings of the 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops!

The Long-Duration Drone Tracking Dataset is part of the paper "Detector-Augmented SAMURAI for Long-Duration Drone Tracking" by Tamara R. Lenhard, Andreas Weinmann, Hichem Snoussi, Tobias Koch. This work was presented in the 2026 IEEE/CVF WACV Workshop on Real-World Surveillance: Applications and Challenges (RWS) and is published in the Proceedings of the 2026 IEEE/CVF WACV Workshops.

Final Version: here

(ArXiv Preprint: here)

Dataset Details

The Long-Duration Drone Tracking Dataset consists of two subsets, R1 and R2, each containing two long-duration sequences. Every sequence is recorded at a resolution of 2040 × 1086 pixels using a ground-mounted Basler acA200-165c camera system equipped with two different lenses (R1: 8 mm lens, R2: 25 mm lens). The recordings were captured in an urban environment characterized by medium-density vegetation and medium-height buildings.

Annotation Format: Annotations (bounding boxes) are provided via text files according to the YOLO standard format

<object-class> <x> <y> <width> <height>

Here, <x> and <y> represent the normalized coordinates of the bounding box center, while <width> and <height> denote the normalized bounding box wisth and height. In SynDroneVision, <object-class> is always 0, indicating the drone class.

Download

Image data and annotations are available via the following links:

Subset Name
Sequence
Size 
Download Link

R1
POS3
7.1 GB
Download R1_POS3

R1
POS7
4.7 GB
Download R1_POS7

R2
POS3
1.8 GB
Download R2_POS3

R2
POS7
5.4 GB
Download R2_POS7

Example images of each sequence are shown below!

Citation

If you find the Long-Duration Drone Tracking Dataset helpful in your research, we kindly ask that you cite the associated paper. Below is the citation in BibTeX format for your convenience:

BibTeX:

@InProceedings{Lenhard_2026_WACV, author = {Lenhard, Tamara R. and Weinmann, Andreas and Snoussi, Hichem and Koch, Tobias}, title = {Detector-Augmented SAMURAI for Long-Duration Drone Tracking}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops}, month = {March}, year = {2026}, pages = {75-84} }