benchmark

KAIST Multispectral Recognition Dataset in Day and Night

Over all days, we successfully captured 50km sequences of synchronized multiple sensors at 25Hz using a fully aligned visible and thermal device, high resolution stereo visible cameras, and high accuracy GPS/IMU inertial navigation system.

Multispectral Pedestrian Detection Benchmark

Geometrically aligned RGB + Thermal images for pedestrian detection

All-Day Visual Place Recognition: Benchmark Dataset and Baselines

We provide the first aligned visible/thermal all-day dataset, including various illumination conditions: day, night, sunset, and sunrise. With this dataset, we introduce multi-spectral loop-detector as a baseline.

Multispectral Pedestrian Detection: Benchmark Dataset and Baselines

We propose a multispectral pedestrian dataset which provides well aligned color-thermal image pairs, captured by beam splitter-based special hardware. With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs.