Fast Object Segmentation Learning with Kernel-based Methods for Robotics
- URL: http://arxiv.org/abs/2011.12805v2
- Date: Fri, 9 Apr 2021 11:55:42 GMT
- Title: Fast Object Segmentation Learning with Kernel-based Methods for Robotics
- Authors: Federico Ceola, Elisa Maiettini, Giulia Pasquale, Lorenzo Rosasco and
Lorenzo Natale
- Abstract summary: Object segmentation is a key component in the visual system of a robot that performs tasks like grasping and object manipulation.
We propose a novel architecture for object segmentation, that overcomes this problem and provides comparable performance in a fraction of the time required by the state-of-the-art methods.
Our approach is validated on the YCB-Video dataset which is widely adopted in the computer vision and robotics community.
- Score: 21.48920421574167
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Object segmentation is a key component in the visual system of a robot that
performs tasks like grasping and object manipulation, especially in presence of
occlusions. Like many other computer vision tasks, the adoption of deep
architectures has made available algorithms that perform this task with
remarkable performance. However, adoption of such algorithms in robotics is
hampered by the fact that training requires large amount of computing time and
it cannot be performed on-line. In this work, we propose a novel architecture
for object segmentation, that overcomes this problem and provides comparable
performance in a fraction of the time required by the state-of-the-art methods.
Our approach is based on a pre-trained Mask R-CNN, in which various layers have
been replaced with a set of classifiers and regressors that are re-trained for
a new task. We employ an efficient Kernel-based method that allows for fast
training on large scale problems. Our approach is validated on the YCB-Video
dataset which is widely adopted in the computer vision and robotics community,
demonstrating that we can achieve and even surpass performance of the
state-of-the-art, with a significant reduction (${\sim}6\times$) of the
training time. The code to reproduce the experiments is publicly available on
GitHub.
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