Real Time Shadow Removal with K-Means Clustering and RGB Color Model

This paper introduces a hybrid approach that is based on color information that utilizes a mask and K-Means clustering algorithm along with the frame averaging background subtraction technique. This hybrid approach efficiently removes artifacts caused by lightening changes such as highlight and reflection from segmentation, while also successfully removing shadows of stationary objects and dark cast shadows. Dark cast shadows cause an issue with tracking and detection. To eradicate these shadows, we first create a mask by assigning values to R, G and B channels utilizing the shadow properties to this RGB color individually, and then we apply K-Means clustering algorithm to this mask for efficient removal. Simulation results from several video sequences with different scene conditions also reveal the effectiveness and robustness of the proposed algorithm.