Person Detection in Thermal Videos Using YOLO
| Autor: | Miran Pobar, Mate Krišto, Marina Ivašić-Kos |
|---|---|
| Přispěvatelé: | Bi, Yaxin, Bhatia, Rahul, Kapoor, Supriya |
| Rok vydání: | 2019 |
| Předmět: |
Person detection
Object Detector Thermal imaging Training set Computer science business.industry Convolutional Neural Networks ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolutional neural network Task (project management) Thermal 0202 electrical engineering electronic engineering information engineering Object detector Convolutional neural networks YOLO RGB color model 020201 artificial intelligence & image processing Computer vision person detection Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030295127 IntelliSys (2) |
| DOI: | 10.1007/978-3-030-29513-4_18 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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