BROWSE
Detail
Conference Paper
ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection
- Authors
- Deokyun Kim, Jeongjun Lee, Jungwon Choi, Jonggeon Park, Giyoung Lee, Yookyung Kim, Myungseok Ki, Juho Lee, Jihun Cha
- Issue Date
- 2026-04
- Citation
- International Conference on Learning Representations (ICLR) 2026, pp.1-36
- Publisher
- ICLR
- Language
- English
- Type
- Conference Paper
- Abstract
- Detecting missing persons in forest environments remains a challenge, as dense canopy cover often conceals individuals from detection in top-down or oblique aerial imagery typically captured by Unmanned Aerial Vehicles (UAVs). While UAVs are effective for covering large, inaccessible areas, their aerial perspectives often miss critical visual cues beneath the forest canopy. This limitation underscores the need for under-canopy perspectives better suited for detecting missing persons in such environments. To address this gap, we introduce ForestPersons, a novel large-scale dataset specifically designed for under-canopy person detection. ForestPersons contains 96,482 images and 204,078 annotations collected under diverse environmental and temporal conditions. Each annotation includes a bounding box, pose, and visibility label for occlusion-aware analysis. ForestPersons provides ground-level and low-altitude perspectives that closely reflect the visual conditions encountered by Micro Aerial Vehicles (MAVs) during forest Search and Rescue (SAR) missions. Our baseline evaluations reveal that standard object detection models, trained on prior large-scale object detection datasets or SAR-oriented datasets, show limited performance on ForestPersons. This indicates that prior benchmarks are not well aligned with the challenges of missing person detection under the forest canopy. We offer this benchmark to support advanced person detection capabilities in real-world SAR scenarios. The dataset is publicly available at https://huggingface.co/datasets/etri/ForestPersons.
- Keyword
- Missing Person Detection, UAV-based Search and Rescue, Forest Environment Dataset
- KSP Keywords
- Bounding Box, Canopy cover, Forest canopy, Large-scale datasets, Large-scale object, Micro Aerial Vehicles, Missing person, Person detection, Real-world, Search and rescue, Visual cues
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
