Pedestrian Detection in Infrared Images Based on Local Shape Features
Abstract
Use of IR images is advantageous for many surveillance applications where the systems must operate around the clock and external illumination is not always available. We investigate the methods derived from visible spectrum analysis for the task of human detection. Two feature classes (edgelets and HOG features) and two classification models(AdaBoost and SVM cascade) are extended to IR images. We find out that it is possible to get detection performance in IR images that is comparable to state-of-the-art results for visible spectrum images. It is also shown that the two domains share many features, likely originating from the silhouettes, in spite of the starkly different appearances of the two modalities.
Cite
Text
Zhang et al. "Pedestrian Detection in Infrared Images Based on Local Shape Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383452Markdown
[Zhang et al. "Pedestrian Detection in Infrared Images Based on Local Shape Features." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/zhang2007cvpr-pedestrian/) doi:10.1109/CVPR.2007.383452BibTeX
@inproceedings{zhang2007cvpr-pedestrian,
title = {{Pedestrian Detection in Infrared Images Based on Local Shape Features}},
author = {Zhang, Li and Wu, Bo and Nevatia, Ram},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2007},
doi = {10.1109/CVPR.2007.383452},
url = {https://mlanthology.org/cvpr/2007/zhang2007cvpr-pedestrian/}
}