FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Video

A Flying Bird Dataset for Surveillance Videos (FBD-SV-2024) is introduced and tailored for the development and performance evaluation of flying bird detection algorithms in surveillance videos. This dataset comprises 483 video clips, amounting to 28,694 frames in total. Among them, 23,833 frames contain 28,366 instances of flying birds. The proposed dataset of flying birds in surveillance videos is collected from realistic surveillance scenarios, where the birds exhibit characteristics such as inconspicuous features in single frames (in some instances), generally small sizes, and shape variability during flight. These attributes pose challenges that need to be addressed when developing flying bird detection methods for surveillance videos. Finally, advanced (video) object detection algorithms were selected for experimentation on the proposed dataset, and the results demonstrated that this dataset remains challenging for the algorithms above.

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Publication Details

Journal
Scientific Data
Published
2025-03-29
DOI
https://doi.org/10.1038/s41597-025-04872-6
Citations
10
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
7.99
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article

FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Video

Zexi Hua, Heng-Chao Li, Zi-Wei Sun, Jinchi Zhang et al.
10 citations
Scientific Data
Remote Sensing and LiDAR Applications
7.99
article

FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Video

Zexi Hua, Heng-Chao Li, Zi-Wei Sun, Jinchi Zhang, Zhipeng Qi, Yan Li, Xiang Li
article en
10 citations

Abstract

A Flying Bird Dataset for Surveillance Videos (FBD-SV-2024) is introduced and tailored for the development and performance evaluation of flying bird detection algorithms in surveillance videos. This dataset comprises 483 video clips, amounting to 28,694 frames in total. Among them, 23,833 frames contain 28,366 instances of flying birds. The proposed dataset of flying birds in surveillance videos is collected from realistic surveillance scenarios, where the birds exhibit characteristics such as inconspicuous features in single frames (in some instances), generally small sizes, and shape variability during flight. These attributes pose challenges that need to be addressed when developing flying bird detection methods for surveillance videos. Finally, advanced (video) object detection algorithms were selected for experimentation on the proposed dataset, and the results demonstrated that this dataset remains challenging for the algorithms above.

Scientific DataVol. 12(1)
Southwest Jiaotong University (CN)
Openalex Percentile: Top 2%
Remote Sensing and LiDAR Applications
7.99
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FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Video — Zexi Hua, Heng-Chao Li, et al. · Scientific Data (2025) | TGRS Research Map | TGRS