Real-world challenges do occur from accidental situations that are not expected at the training phase. In this paper, we address a practical multispectral fusion issue as unexpected image contamination in day and night conditions.
Geometrically aligned RGB + Thermal images for pedestrian detection
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.
we propose a two-phase classifier integrating an existing baseline detector and a hard negative expert by separately conquering recall and precision. By virtue of the two-phase structure, our method achieve competitive performance with only little additional computation.