Robust outdoor human segmentation based on color-based statistical approach and edge combination

The statistical background subtraction and shadow detection algorithm (SBGS) is fast and reliable in outdoor scenes with shadows. However, its reliability depends on the number of training frames to construct the initial background model. In addition, the similarity between foreground and background colors, i.e, camouflage problem, could lead to the worse performance of background subtraction. In this paper, we present a robust outdoor background subtraction technique based on color statistics and edge information. Vector median filtering technique was employed in the initialization of the background model to address the SBGS's limitation. In addition, a combination of color statistics and edge information is utilized to improve the segmentation results over the original algorithm. Test data was compiled from various outdoor conditions including strong shadow, complex background, and low contrast scenes. The background subtraction results show that the proposed approach outperformed other well-known segmentation algorithms such as non-adaptive and adaptive SBGS algorithms as well as mixture of Gaussian algorithm based on precision-recall and computational measurements.