• Corpus ID: 18404693

Detection and Tracking of Liquids with Fully Convolutional Networks

@article{Schenck2016DetectionAT,
  title={Detection and Tracking of Liquids with Fully Convolutional Networks},
  author={Connor Schenck and Dieter Fox},
  journal={ArXiv},
  year={2016},
  volume={abs/1606.06266}
}
Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids. We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent network. Our results show that the best liquid detection results are achieved when aggregating data over… 

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