Pedestrian detection is an important problem in computer vision due to its importance for applications such as visual surveillance, robotics, and automotive safety. This paper pushes the state-of-the-art of pedestrian detection in two ways. First, we propose a simple yet highly eﬀective novel feature based on binocular disparity, outperforming previously proposed stereo features. Second, we show that the combination of diﬀerent classiﬁers often improves performance even when classiﬁers are based on the same feature or feature combination. These two extensions result in signiﬁcantly improved performance over the state-of-the-art on two challenging datasets.