Self-supervised Object Tracking with Cycle-consistent Siamese Networks
- URL: http://arxiv.org/abs/2008.00637v1
- Date: Mon, 3 Aug 2020 04:10:38 GMT
- Title: Self-supervised Object Tracking with Cycle-consistent Siamese Networks
- Authors: Weihao Yuan, Michael Yu Wang, Qifeng Chen
- Abstract summary: We exploit an end-to-end Siamese network in a cycle-consistent self-supervised framework for object tracking.
We propose to integrate a Siamese region proposal and mask regression network in our tracking framework so that a fast and more accurate tracker can be learned without the annotation of each frame.
- Score: 55.040249900677225
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Self-supervised learning for visual object tracking possesses valuable
advantages compared to supervised learning, such as the non-necessity of
laborious human annotations and online training. In this work, we exploit an
end-to-end Siamese network in a cycle-consistent self-supervised framework for
object tracking. Self-supervision can be performed by taking advantage of the
cycle consistency in the forward and backward tracking. To better leverage the
end-to-end learning of deep networks, we propose to integrate a Siamese region
proposal and mask regression network in our tracking framework so that a fast
and more accurate tracker can be learned without the annotation of each frame.
The experiments on the VOT dataset for visual object tracking and on the DAVIS
dataset for video object segmentation propagation show that our method
outperforms prior approaches on both tasks.
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