Multispectral pedestrian detection: Benchmark dataset and baseline

• Published in: 2015 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
• Language: English
• Published: IEEE 01.06.2015
• Subjects: Beams (radiation); Calibration; Cameras; Channels; Color; Computer vision; Conferences; Detectors; Hardware; Histograms; Image color analysis; Pattern recognition; Pedestrians
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2015.7298706

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.