TinyPedSeg: A Tiny Pedestrian Segmentation Benchmark for Top-Down Drone Images

Yusuf H. Sahin*, Elvin Abdinli, M. Arda Aydin, Gozde Unal

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Özet

The usage of Unmanned Aerial Vehicles (UAVs) has significantly increased in various fields such as surveillance, agriculture, transportation, and military operations. However, the integration of UAVs in these applications requires the ability to navigate autonomously and detect/segment objects in real-time, which can be achieved through the use of neural networks. Despite object detection for RGB images/videos obtained from UAVs are widely studied before, limited effort has been made for segmentation from top-down aerial images. Considering the case in which the UAV is extremely high from the ground, the task can be formed as tiny object segmentation. Thus, inspired from the TinyPerson dataset which focuses on person detection from UAVs, we present TinyPedSeg, which contains 2563 pedestrians in 320 images. Specialized only in pedestrian segmentation, our dataset presents more informativeness than other UAV segmentation datasets. The dataset and the baseline codes are available at https://github.com/ituvisionlab/tinypedseg

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings of MVA 2023 - 18th International Conference on Machine Vision and Applications
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9784885523434
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik18th International Conference on Machine Vision and Applications, MVA 2023 - Hamamatsu, Japan
Süre: 23 Tem 202325 Tem 2023

Yayın serisi

AdıProceedings of MVA 2023 - 18th International Conference on Machine Vision and Applications

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???event.eventtypes.event.conference???18th International Conference on Machine Vision and Applications, MVA 2023
Ülke/BölgeJapan
ŞehirHamamatsu
Periyot23/07/2325/07/23

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Publisher Copyright:
© 2023 IEICE.

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