ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection

TL;DR

ForestPersons is a novel large-scale dataset specifically designed for under-canopy person detection that provides ground-level and low-altitude perspectives that closely reflect the visual conditions encountered by Micro Aerial Vehicles during forest Search and Rescue (SAR) missions.

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.

Figures (24)

• Figure 1: Comparison of two UAV-based person search scenarios. (a) High-altitude views offer wide-area coverage but often fail to detect targets due to canopy occlusion. (b) Low-altitude MAVs provide closer, ground-level views beneath the canopy, improving the chances of spotting missing persons despite vegetation occlusion.
• Figure 2: Overview of ForestPersons composition pipeline. The full process from data collection in forest environments to frame sampling from video sequences, bounding boxes annotation of missing persons, and difficulty-aware dataset splitting.
• Figure 3: Visual samples from ForestPersons. Images depicting individuals in diverse poses, occlusion levels, seasons, and forest environments.
• Figure 4: Annotation statistics of ForestPersons. Instance-level distribution for pose and visibility (Top) and image-level distribution for season, place, and weather (Bottom).
• Figure 5: ForestPersons samples by difficulty level. Shown are representative video sequences from the easy, medium, and hard groups. Predicted boxes (green) are shown with confidence scores, and ground-truth boxes (other colors) are labeled as {pose}_{visibility level}.