Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advan- tages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines mul- tiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based repre- sentations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to inte- grate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an ac- ceptable efficiency. In particular, the resulting detector op- erates at five frames per second using a laptop machine. We tested the proposed method with well-known challeng- ing datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one.