In this paper, we propose a novel edge descriptor method for background modeling. In comparison to previous edge-based local-pattern methods, it is more robust to noise and illumination variations due to the use of principal gradient information in a local neighborhood. For the background modeling problem, we combined the proposed method with the Local Hybrid Pattern and experimented with an adaptive-dictionary-model based background modeling method. We show in the quantitative evaluations that the proposed methods is better than other local edge descriptors when applied to the same framework. Furthermore, we show that our proposed method is more powerful than other state of the art methods on standard datasets for the background modeling problem.