CoralVOS: Dataset and Benchmark for Coral Video Segmentation
Zheng Ziqiang Xie Yaofeng Liang Haixin Yu Zhibin Sai-Kit Yeung
Coral reefs formulate the most valuable and productive marine ecosystems,
providing habitat for many marine species. Coral reef surveying and analysis
are currently confined to coral experts who invest substantial effort in
generating comprehensive and dependable reports (\emph{e.g.}, coral coverage,
population, spatial distribution, \textit{etc}), from the collected survey
data. However, performing dense coral analysis based on manual efforts is
significantly time-consuming, the existing coral analysis algorithms compromise
and opt for performing down-sampling and only conducting sparse point-based
coral analysis within selected frames. However, such down-sampling will
\textbf{inevitable} introduce the estimation bias or even lead to wrong
results. To address this issue, we propose to perform \textbf{dense coral video
segmentation}, with no down-sampling involved. Through video object
segmentation, we could generate more \textit{reliable} and \textit{in-depth}
coral analysis than the existing coral reef analysis algorithms. To boost such
dense coral analysis, we propose a large-scale coral video segmentation
dataset: \textbf{CoralVOS} as demonstrated in Fig. 1. To the best of our
knowledge, our CoralVOS is the first dataset and benchmark supporting dense
coral video segmentation. We perform experiments on our CoralVOS dataset,
including 6 recent state-of-the-art video object segmentation (VOS) algorithms.
We fine-tuned these VOS algorithms on our CoralVOS dataset and achieved
observable performance improvement. The results show that there is still great
potential for further promoting the segmentation accuracy. The dataset and
trained models will be released with the acceptance of this work to foster the
coral reef research community.