Abstract
This paper presents a physics-based approach capable of detecting cast shadows in video sequence effectively. We develop a new physical model of cast shadows without making prior assumption of the spectral power distribution (SPD) of the light sources and ambient illumination in the scene. The background appearance variation caused by cast shadows is characterized as the interaction of the blocked light sources and the background surface reflectance. We then take advantage of the statistical prevalence of cast shadows to learn and update the shadow model parameters using the Gaussian mixture model (GMM) over time. The proposed algorithm is completely unsupervised and can adapt to specific environment with complex illumination condition as well as changing shadow conditions. Experimental results on three challenging sequences demonstrate the effectiveness of the proposed method.