FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Video
Zi-Wei Sun, Ze-Xi Hua, Heng-Chao Li, Zhi-Peng Qi, Xiang Li, Yan Li,, and Jin-Chi Zhang

TL;DR
The paper introduces the FBD-SV-2024 dataset, a comprehensive collection of surveillance videos featuring flying birds, designed to evaluate and improve bird detection algorithms in challenging real-world scenarios.
Contribution
It provides a new, publicly available dataset of flying birds in surveillance videos, addressing challenges like small size and shape variability, and evaluates existing detection algorithms on it.
Findings
Existing algorithms find the dataset challenging due to bird characteristics.
The dataset highlights the need for improved detection methods.
Baseline results demonstrate room for advancement in flying bird detection.
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…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · Infrared Target Detection Methodologies · Animal Vocal Communication and Behavior
