/A Probabilistic Framework for Car Detection in Images using Context and Scale

A Probabilistic Framework for Car Detection in Images using Context and Scale

David Held, Jesse Levinson and Sebastian Thrun
Conference Paper, International Conference on Robotics and Automation (ICRA), May, 2012

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Abstract

Detecting cars in real-world images is an important task for autonomous driving, yet it remains unsolved. The system described in this paper takes advantage of context and scale to build a monocular single-frame image-based car detector that significantly outperforms the baseline. The system uses a probabilistic model to combine multiple forms of evidence for both context and scale to locate cars in a real-world image.We also use scale filtering to speed up our algorithm by a factor of 3.3 compared to the baseline. By using a calibrated camera and localization on a roadmap, we are able to obtain context and scale information from a single image without the use of a 3D laser. The system outperforms the baseline by an absolute 9.4% in overall average precision and 11.7% in average precision for cars smaller than 50 pixels in height, for which context and scale cues are especially important.

BibTeX Reference
@conference{Held-2012-103061,
author = {David Held and Jesse Levinson and Sebastian Thrun},
title = {A Probabilistic Framework for Car Detection in Images using Context and Scale},
booktitle = {International Conference on Robotics and Automation (ICRA)},
year = {2012},
month = {May},
}
2017-12-07T16:30:56+00:00