Camera Elevation Estimation from a Single Mountain Landscape Photograph

TL;DR

This paper introduces Alps100K, a large dataset for camera elevation estimation from single mountain landscape photos, and presents two data-driven methods that outperform human performance in this task.

Contribution

The paper provides a new benchmark dataset and compares convolutional neural networks with local features for elevation estimation, demonstrating superior performance over humans.

Findings

Both methods outperform humans in elevation estimation.

Combining the methods yields the best accuracy.

Alps100K enables large-scale evaluation of elevation estimation techniques.

Abstract

This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment. We introduce a new benchmark dataset of one-hundred thousand images with annotated camera elevation called Alps100K. We propose and experimentally evaluate two automatic data-driven approaches to camera elevation estimation: one based on convolutional neural networks, the other on local features. To compare the proposed methods to human performance, an experiment with 100 subjects is conducted. The experimental results show that both proposed approaches outperform humans and that the best result is achieved by their combination.