Shadow Detection in Dynamic Scenes Using Dense Stereo Information and an Outdoor Illumination Model

We present a system for detecting shadows in dynamic outdoor scenes. The technique is based on fusing background subtraction operations performed on both color and disparity data, respectively. A simple geometrical analysis results in an ability to classify pixels into foreground, shadow candidate, and background. The shadow candidates are further refined by analyzing displacements in log chromaticity space to find the shadow hue shift with the strongest data support and ruling out other displacements. This makes the shadow detection robust towards false positives from rain, for example. The techniques employed allow for 3Hz operation on commodity hardware using a commercially available dense stereo camera solution.