ArticleSensors (Basel, Switzerland)2023
Real-Time Person Detection in Wooded Areas Using Thermal Images from an Aerial Perspective.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 11 citations in OpenAlex.
- Human Detection in UAV Thermal Imagery: Dataset Extension and Comparative Evaluation on Embedded Platforms.Journal of imaging · 2025Article
- Advancing Remote Life Sensing for Search and Rescue: A Novel Framework for Precise Vital Signs Detection via Airborne UWB Radar.Sensors (Basel, Switzerland) · 2025Article
- A Novel Entropy-Based Approach for Thermal Image Segmentation Using Multilevel Thresholding.Entropy (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting people in images and videos captured from an aerial platform in wooded areas for search and rescue operations is a current problem. Detection is difficult due to the relatively small dimensions of the person captured by the sensor in relation to the environment. The environment can generate occlusion, complicating the timely detection of people. There are currently numerous RGB image datasets available that are used for person detection tasks in urban and wooded areas and consider the general characteristics of a person, like size, shape, and height, without considering the occlusion of the object of interest. The present research work focuses on developing a thermal image dataset, which considers the occlusion situation to develop CNN convolutional deep learning models to perform detection tasks in real-time from an aerial perspective using altitude control in a quadcopter prototype. Extended models are proposed considering the occlusion of the person, in conjunction with a thermal sensor, which allows for highlighting the desired characteristics of the occluded person.
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Registered trials
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