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Multispectral pedestrian detection: Benchmark dataset and baseline

Cited 541 time in Web of Science Cited 726 time in Scopus
Authors

Hwang, Soonmin; Park, Jaesik; Kim, Namil; Choi, Yukyung; Kweon, In So

Issue Date
2015
Publisher
IEEE
Citation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.07-12-June-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. Multi-spectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.
ISSN
1063-6919
URI
https://hdl.handle.net/10371/201327
DOI
https://doi.org/10.1109/CVPR.2015.7298706
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning

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