Abstract
In this paper, the task of automatic person detection in thermal images using convolutional neural network-based models originally intended for detection in RGB images is investigated. The performance of the standard YOLOv3 model is compared with a custom trained model on a dataset of thermal images extracted from videos recorded at night in clear weather, rain and fog, at different ranges and with different types of movement – running, walking and sneaking. The experiments show excellent results in terms of average precision for all tested scenarios, and a significant improvement of performance for person detection in thermal imaging with a modest training set.
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Acknowledgment
This research was fully supported by the Croatian Science Foundation under the project IP-2016-06-8345 “Automatic recognition of actions and activities in multimedia content from the sports domain” (RAASS).
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Ivasic-Kos, M., Kristo, M., Pobar, M. (2020). Person Detection in Thermal Videos Using YOLO. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_18
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DOI: https://doi.org/10.1007/978-3-030-29513-4_18
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