Connor Greenwell

A Fast Method for Estimating Transient Scene Attributes

Ryan Baltenberger, Menghua Zhai, Connor Greenwell, Scott Workman, Nathan Jacobs. (WACV, 2018)

Abstract

We propose to use deep convolutional neural networks to estimate the transient attributes of a scene from a single image. Transient scene attributes describe both the objective conditions, such as the weather, time of day, and the season, and subjective properties of a scene, such as whether or not the scene seems busy. Recently, convolutional neural networks have been used to achieve state-of-the-art results for many vision problems, from object detection to scene classification, but have not previously been used for estimating transient attributes. We compare several methods for adapting an existing network architecture and present state-of-the-art results on two benchmark datasets. Our method is more accurate and significantly faster than all previous methods, enabling real-world applications.

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Bibtex

@inproceedings{baltenberger2016deeptransient,
  author = {Ryan Baltenberger and Menghua Zhai and Connor Greenwell
    and Scott Workman and Nathan Jacobs},
  title = {{A Fast Method for Estimating Transient Scene Attributes}},
  year = 2016,
  booktitle = {{IEEE Winter Conference on Applications of Computer Vision (WACV)}},
  keywords = {conference}
}