The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We address the problem of image-based scene analysis from streaming video, as would be seen from a moving platform, in order to efficiently generate spatially and temporally consistent predictions of semantic categories over time. In contrast to previous techniques which typically address this problem in batch and/or through graphical models, we demonstrate that by learning visual similarities between pixels across frames, a simple filtering algorithm is able to achieve high performance predictions in an efficient and online/causal manner. Our technique is a meta-algorithm that can be efficiently wrapped around any scene analysis technique that produces a per-pixel semantic category distribution. We validate our approach over three different scene analysis techniques on three different datasets that contain different semantic object categories. Our experiments demonstrate that our approach is very efficient in practice and substantially improves the consistency of the predictions over time.

Notes

Sponsor: ARL-Collaborative Technology Alliance Program

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

Associated Project(s): CTA Robotics

Number of pages: 7

Text Reference

Ondrej Miksik, Daniel Munoz, J. Andrew (Drew) Bagnell, and Martial Hebert, "Efficient Temporal Consistency for Streaming Video Scene Analysis," IEEE International Conference on Robotics and Automation (ICRA), May, 2013.

BibTeX Reference

@inproceedings{Miksik_2013_7396,
   author = "Ondrej Miksik and Daniel Munoz and J. Andrew (Drew) Bagnell and Martial Hebert",
   title = "Efficient Temporal Consistency for Streaming Video Scene Analysis",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA)",
   month = "May",
   year = "2013",
}

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