Bird-Count: a multi-modality benchmark and system for bird population counting in the wild
Name
11042_2023_14833_ReferencePDF.pdf
Size
8.64 MB
Format
Adobe PDF
Checksum (MD5)
b9e9cb7e8d586ece462e1a1b37ef355e
Author(s)
Wang, Hongchang
Lu, Huaxiang
Guo, Huimin
Jian, Haifang
Gan, Chuang
Liu, Wu
Date Issued
April 29, 2023
Publisher
Springer US
Citation
Wang, Hongchang, Lu, Huaxiang, Guo, Huimin, Jian, Haifang, Gan, Chuang et al. 2023. "Bird-Count: a multi-modality benchmark and system for bird population counting in the wild."
Version
Author's final manuscript
Abstract
The fluctuation of the bird population reflects the change in the ecosystem, which plays a vital role in ecosystem conservation. However, manual counting is still the mainstream method for bird population counting, which is time-consuming and laborious. One major bottleneck in developing efficient, accurate, and intelligent learning algorithms to counting birds is the lack of large-scale datasets. In this paper, the first large-scale bird population counting dataset, named Bird-Count, with multi-modality morphology annotations is proposed. This paper first evaluates various state-of-the-art (SOTA) models for crowd counting on the Bird-Count and gets poor results. The reason is that the forms, appearances, and postures among different birds are more variant than the crowd. To mitigate these challenges, a simple yet effective plug-and-play framework, called Morphology Prior Knowledge Fusion Network (MPKNet), which can be used on-site to help generate a high-precision bird population density map by incorporating morphological prior knowledge, is proposed. Comprehensive evaluations show that the proposed method can reduce the error rate by 6.02% compared with the current SOTA crowd counting algorithms on average. Moreover, with the above technologies, the intelligent bird population monitoring system is deployed in several important wetland national nature reserves for bird protection.
MIT Department
MIT-IBM Watson AI Lab
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11042-023-14833-z
