Multispectral pedestrian detection: Benchmark dataset and baseline

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...

Full description

Saved in:
Bibliographic Details
Published in2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1037 - 1045
Main Authors Hwang, Soonmin, Park, Jaesik, Kim, Namil, Choi, Yukyung, Kweon, In So
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Beams (radiation)
Calibration
Cameras
Channels
Color
Computer vision
Conferences
Detectors
Hardware
Histograms
Image color analysis
Pattern recognition
Pedestrians
Online AccessGet full text

Cover

More Information
Summary: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. Multi-spectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
2575-7075
DOI:10.1109/CVPR.2015.7298706