A differential correction based shadow removal method for real-time monitoring

Shadow removal is an important issue in the field of motion object surveillance and automatic control. Although many works are concentrated on this issue, the diverse and similar motion patterns between shadows and objects still severely affect the removal performance. Constrained by the computational efficiency in real-time monitoring, the pixel feature based methods are still the main shadow removal methods in practice. Following this idea, this paper proposes a novel and simple shadow removal method based on a differential correction calculation between the pixel values of Red, Green and Blue channels. Specifically, considering the fact that shadows are formed because of the occlusion of light by objects, all the reflected light will be attenuated. Hence there will be a similar weakening trends in all Red, Green and Blue channels of the shadow areas, but not in the object areas. These trends can be caught by differential correction calculation and distinguish the shadow areas from object areas. Based on this feature, our shadow removal method is designed. Experiment results verify that, compared with other state-of-the-art shadow removal methods, our method improves the average of object and shadow detection accuracies by at least 10% in most of the cases.