Datasets:

etri
/

ForestPersonsIR

license: cc-by-nc-sa-4.0
task_categories:
  - object-detection
language:
  - en
tags:
  - image
  - drone
  - uav
  - search-and-rescue
  - person
  - mav
pretty_name: forestpersonsIR-v1
size_categories:
  - 10K<n<100K
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     - **Commercial Use**: Requires a separate license. Contact **deokyunKim@etri.re.kr**.
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ForestPersonsIR Dataset

Dataset Summary

ForestPersonsIR is a large-scale dataset designed for missing person detection in forested environments under search-and-rescue scenarios. The dataset simulates realistic field conditions with varied poses (standing, sitting, lying) and visibility levels (20, 40, 70, 100). Images were captured using IR sensors at ground and low-altitude perspectives.

Supported Tasks

Object Detection
Search and Rescue Benchmarking
Robust Detection under Dense Canopy Conditions

Languages

Visual data only (no textual data)

Dataset Structure

Data Fields

image: IR image
annotations: COCO-style bounding boxes with the following attributes:
- Bounding box coordinates
- Category: person
- Visibility ratio (20, 40, 70, 100)
- Pose (standing, sitting, lying)

Data Splits

Split	# Images	# Annotations
Train	44,857	59,715
Validation	7,094	7,151
Test	12,191	13,124

Total images: 64,142

Dataset Creation

Collection Process

Data was collected during controlled simulations of missing persons in forested areas, using human subjects posing realistically. All images were taken from heights of 1.5m to 2.0m, mimicking UAV perspectives, and were captured with:

FLIR Boson
FLIR Boson+

Tripods were employed when drone use was impractical for safety reasons.

Annotation Process

Annotations were manually created using COCO Annotator by trained annotators.

Note on Indexing

Please note that there is no sample with index 311 in this dataset. This index was intentionally skipped during dataset construction due to internal filtering steps. This does not affect dataset integrity or model training in any way.

Usage Example

(Recommended) Full Download — COCO Format Ready

# Clone the dataset repo (includes CSV + annotations.zip + dataset.py)
git lfs install
git clone https://huggingface.co/datasets/etri/ForestPersons
cd ForestPersons

# Download and extract all images (already included in the repo)
# Structure: images/{folder}/{image}.jpg

# Unzip COCO-style annotations
unzip annotations.zip 

# Resulting directory:
# ├── images/
# ├── annotations/
# │   ├── train.json
# │   ├── val.json
# │   └── test.json

License

The ForestPersons Dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

Under this license, you may use, share, and adapt the dataset for non-commercial purposes, provided you give appropriate credit and distribute any derivatives under the same license.

For full license terms, please refer to the LICENSE file.

If you have questions regarding the dataset or its usage, please contact:

deokyunkim@etri.re.kr

Additional Terms Regarding Trained Models

Any AI models, algorithms, or systems trained, fine-tuned, or developed using the ForestPersons Dataset are strictly limited to non-commercial use.

Disclaimer

The ForestPersons Dataset is provided "as is" without any warranty of any kind, either express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, and non-infringement.

The authors and affiliated institutions shall not be held liable for any damages arising from the use of the dataset.

Citation Information

If you are using this dataset, please cite

@inproceedings{kim2026forestpersons,
  title     = {ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection},
  author    = {Deokyun Kim and Jeongjun Lee and Jungwon Choi and Jonggeon Park and Giyoung Lee and Yookyung Kim and Myungseok Ki and Juho Lee and Jihun Cha},
  booktitle = {The Fourteenth International Conference on Learning Representations (ICLR)},
  year      = {2026},
  url       = {https://huggingface.co/datasets/etri/ForestPersons},
}

Deokyun Kim, Jeongjun Lee, Jungwon Choi, and Jonggeon Park contributed equally to this work. Specific roles are as follows:

Manuscript and Methodology: The research design, experimental analysis, and manuscript writing were performed by Deokyun Kim, Jeongjun Lee, Jungwon Choi, Jonggeon Park, Giyoung Lee, Juho Lee, and Jihun Cha.
Dataset Curation: The acquisition of forest environment data and the subsequent annotation process were conducted by Deokyun Kim, Yookyung Kim, and Myungseok Ki.

Acknowledgments

This work was supported by the Institute of Information & communications Technology Planning Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-II220021, Development of Core Technologies for Autonomous Searching Drones)