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.
CITATION STYLE
Ivasic-Kos, M., Kristo, M., & Pobar, M. (2020). Person detection in thermal videos using YOLO. In Advances in Intelligent Systems and Computing (Vol. 1038, pp. 254–267). Springer Verlag. https://doi.org/10.1007/978-3-030-29513-4_18
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