Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning - Publication - Bridge of Knowledge

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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning

Abstract

Every year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and machine learning. Two types of cameras, RGB and thermal, were integrated and calibrated to form a multimodal imaging system. The system was designed and implemented to acquire real-world data for bathing people in swimming pools. The EfficientDet models were adapted and trained on collected data reaching at least 94% detection accuracy, with the highest result equal to 97.47%. The best accuracy obtained for the thermal data was lower and equal to 94.85%. However, thermal imaging allows observing scenes in low-light conditions or darkness. This could potentially highly improve the effectiveness of rescue missions, decreasing the death rates or improving the health of early rescued people. Thermal imaging could also be more acceptable regarding privacy, as high-frequency biometric features are not as easy to extract from thermal images as from high-resolution RGB images.

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Details

Category:
Monographic publication
Type:
rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Language:
English
Publication year:
2024
Bibliographic description:
Konert J., Dradrach A., Rumiński J.: Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning// The Latest Developments and Challenges in Biomedical Engineering/ : Springer, 2024, s.431-442
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-031-38430-1
Sources of funding:
  • Statutory activity/subsidy
Verified by:
Gdańsk University of Technology

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