One of main challenges of driver assistance systems is to detect multi-occluded pedestrians in real-time in complicated scenes, to reduce the number of traffic accidents. In order to improve the accuracy and speed of detection system, we proposed a real-time multi-occluded pedestrian detection algorithm based on R-FCN. RoI Align layer was introduced to solve misalignments between the feature map and RoI of original images. A separable convolution was optimized to reduce the dimensions of position-sensitive score maps, to improve the detection speed. For occluded pedestrians, a multi-scale context algorithm is proposed, which adopt a local competition mechanism for adaptive context scale selection. For low visibility of the body occlusion, deformable RoI pooling layers were introduced to expand the pooled area of the body model. Finally, in order to reduce redundant information in the video sequence, Seq-NMS algorithm is used to replace traditional NMS algorithm. The experiments have shown that there is low detection error on the datasets Caltech and ETH, the accuracy of our algorithm is better than that of the detection algorithms in the sets, works particularly well with occluded pedestrians.