ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection
This addresses the problem of improving search and rescue capabilities in forest environments for SAR teams, though it is incremental as it primarily provides a new dataset rather than a novel detection method.
The authors tackled the problem of detecting missing persons in forest environments where dense canopy cover limits aerial detection, by introducing ForestPersons, a large-scale dataset with 96,482 images and 204,078 annotations for under-canopy person detection. Baseline evaluations showed standard object detection models performed poorly on this dataset, indicating prior benchmarks are misaligned with these challenges.
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.