Hot or Not: Exploring Correlations

Between Appearance and Temperature


Daniel Glasner, Pascal Fua, Todd Zickler and Lihi Zelnik-Manor

 

Abstract

In this paper we explore interactions between the appearance of an outdoor scene and the ambient temperature. By studying statistical correlations between image sequences from outdoor cameras and temperature measurements we identify two interesting interactions. First, semantically meaningful regions such as foliage and reflective oriented surfaces are often highly indicative of the temperature. Second, small camera motions are correlated with the temperature in some scenes. We propose simple scene-specific temperature prediction algorithms which can be used to turn a camera into a crude temperature sensor. We find that for this task, simple features such as local pixel intensities outperform sophisticated, global features such as from a semantically-trained convolutional neural network.

 

Paper

“Hot or Not: Exploring Correlations Between Appearance and Temperature”, ICCV 2015 [pdf] [BibTex]

 

Spotlight

 

Dataset

The dataset described in our paper is available for download:
[README], [data]
[precomputed global features]
[precomputed prediction results]
[noisy pairwise alignment].

 

Code

Code to reporduce the experiments described in our paper is available for download:
[README], [code].