A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1606.00487v3.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Recurrent Fully Convolutional Networks for Video Segmentation
[article]
<span title="2016-10-31">2016</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
Preserved Fulltext
Other Versions
<span title="2016-06-09">2016</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.00487v2">arXiv:1606.00487v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e7r32s6dhfhn3pvppl6inqauny">fatcat:e7r32s6dhfhn3pvppl6inqauny</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.00487v2">arXiv:1606.00487v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e7r32s6dhfhn3pvppl6inqauny">fatcat:e7r32s6dhfhn3pvppl6inqauny</a> </span>
<span title="2016-06-01">2016</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.00487v1">arXiv:1606.00487v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fxdr674xi5apbetpgjw76wbc5q">fatcat:fxdr674xi5apbetpgjw76wbc5q</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.00487v1">arXiv:1606.00487v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fxdr674xi5apbetpgjw76wbc5q">fatcat:fxdr674xi5apbetpgjw76wbc5q</a> </span>
Image segmentation is an important step in most visual tasks. While convolutional neural networks have shown to perform well on single image segmentation, to our knowledge, no study has been been done on leveraging recurrent gated architectures for video segmentation. Accordingly, we propose a novel method for online segmentation of video sequences that incorporates temporal data. The network is built from fully convolutional element and recurrent unit that works on a sliding window over the
<span class="external-identifiers">
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.00487v3">arXiv:1606.00487v3</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5uh63wn37rbshk6nriec3iu23y">fatcat:5uh63wn37rbshk6nriec3iu23y</a>
</span>
more »
... poral data. We also introduce a novel convolutional gated recurrent unit that preserves the spatial information and reduces the parameters learned. Our method has the advantage that it can work in an online fashion instead of operating over the whole input batch of video frames. The network is tested on the change detection dataset, and proved to have 5.5\% improvement in F-measure over a plain fully convolutional network for per frame segmentation. It was also shown to have improvement of 1.4\% for the F-measure compared to our baseline network that we call FCN 12s.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200902161640/https://arxiv.org/pdf/1606.00487v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/3d/04/3d04bc62a30f4d6329db2099cc318cb89974a872.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.00487v3" title="arxiv.org access">
<button class="ui compact blue labeled icon button serp-button">
<i class="file alternate outline icon"></i>
arxiv.org
</button>
</a>