Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo
Abstract
Pedestrian detection is an important problem in computer vision due to its importance for applications such as visual surveillance, robotics, and automotive safety. This paper pushes the state-of-the-art of pedestrian detection in two ways. First, we propose a simple yet highly effective novel feature based on binocular disparity, outperforming previously proposed stereo features. Second, we show that the combination of different classifiers often improves performance even when classifiers are based on the same feature or feature combination. These two extensions result in significantly improved performance over the state-of-the-art on two challenging datasets.
Cite
Text
Walk et al. "Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15567-3_14Markdown
[Walk et al. "Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/walk2010eccv-disparity/) doi:10.1007/978-3-642-15567-3_14BibTeX
@inproceedings{walk2010eccv-disparity,
title = {{Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo}},
author = {Walk, Stefan and Schindler, Konrad and Schiele, Bernt},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {182-195},
doi = {10.1007/978-3-642-15567-3_14},
url = {https://mlanthology.org/eccv/2010/walk2010eccv-disparity/}
}