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Abstract
Efficient pedestrian detection is a very important task in ensuring safety within road conditions, especially after sunset. One way to achieve this goal is to use thermal imaging in conjunction with deep learning methods and an annotated dataset for models training. In this work, such a dataset has been created by capturing thermal images of pedestrians in different weather and traffic conditions. All images were manually annotated with bounding boxes. As a result, the created dataset consists of 9178 annotated, thermal images that can be used in many applications including nighttime pedestrian detection.
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Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Górska A., Guzal P., Wędołowska A., Włoszczyńska M., Rumiński J.: AITP - AI Thermal Pedestrians Dataset// / : , 2022,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/hsi55341.2022.9869478
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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