This paper proposed a method to concatenate a cell-structured Local Binary Pattern(LBP) feature into Histogram of Gradients(HOG) to solve the problem that HOG was vulnerable to the interference of vertical background gradient information in pedestrian detection. Firstly, the detection window was divided into 16×16 non-overlapping blocks, then the LBP histogram of each block was calculated and his parameters were obtained by extensive experiments. Afterwards, the HOG was computed by the optimized interpolation method, and it was combined with LBP histogram to constitute a joint histogram. Finally, a discriminative model was trained by Bootstrapped linear Support Vector Machine(SVM). Based on the test of the INRIA pedestrian dataset, it is shown that the detection rate has been increased from 89% of the HOG feature to 95% when False Positive Per Window(FPPW) is 10-4, and the detection speed has been raised from 0.625 to 0.533 ms per window. It is concluded that the proposed method in this paper eliminates the false detection caused by the interference of gradient information and improves the detection rate by describing both contour and texture information.