Multispectral pedestrian detection: Benchmark dataset and baseline
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.
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The KAIST multispectral pedestrian dataset was collected in Seoul.
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From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [90]
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“Also, the coefficient λ ce and λ dice are set to 5.0 11113 TABLE II: Quantitative comparisons for semantic segmentation of RGB-T images on MF [11], PST900 [35], and KP [14] datasets.”
BackgroundMethodResultInfluentialSelf-citation
- Authors
- Hwang, S., Park, J., Kim, N., Choi, Y., & Kweon, I. S.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1037-1045
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1910108985
- Cited by
- 1,249
- References
- 1
- Citations by year
- 2026: 141 · 2025: 213 · 2024: 174 · 2023: 163 · 2022: 161 · 2021: 116 · 2020: 87 · 2019: 76 · 2018: 53 · 2017: 40 · 2016: 21 · 2015: 4
- FWCI
- 16.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- pedestrian detection, multispectral imaging, infrared thermography, benchmark dataset, temporal correspondence
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