Linear colour correction for multiple illumination changes and non‐overlapping cameras

Many image processing methods, such as techniques for people re‐identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per‐location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter‐camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto‐exposure algorithm found in the recent literature plus a colour correction one based on the inverse‐intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state‐of‐the‐art algorithms.