Multispectral Pedestrian Detection: Benchmark Dataset and Baseline

Soonmin Hwang, Jaesik Park, Namil Kim, Yukyung Choi, In So Kweon; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1037-1045

Abstract


With the increasing interest in pedestrian detection, pedestrian datasets have also been the subject of research in the past decades. However, most existing datasets focus on a color channel, while a thermal channel is helpful for detection even in a dark environment. With this in mind, we propose a multispectral pedestrian dataset which provides well aligned color-thermal image pairs, captured by beam splitter-based special hardware. The color-thermal dataset is as large as previous color-based datasets and provides dense annotations including temporal correspondences. With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs. Multispectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.

Related Material


[pdf]
[bibtex]
@InProceedings{Hwang_2015_CVPR,
author = {Hwang, Soonmin and Park, Jaesik and Kim, Namil and Choi, Yukyung and So Kweon, In},
title = {Multispectral Pedestrian Detection: Benchmark Dataset and Baseline},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}