We propose a cascade of two complementary features to detect pedestrians from static images quickly and accurately. Co-occurrence Histograms of Oriented Gradients (CoHOG) descriptors have a strong classification capability but are extremely high dimensional. On the other hand, Haar-like features are computationally efficient but not highly discriminative for extremely varying texture and shape information such as pedestrians with different clothing and stances. Therefore, the combination of both features enables fast and accurate pedestrian detection. Our framework comprises a cascade of Haar based weak classifiers followed by a CoHOG-SVM classifier. The experimental results on the DaimlerChrysler and INRIA benchmark datasets show that we can reach very close accuracy to the most accurate CoHOG-only classifier but in less than 1/200 of its computational cost. Additionally, we show that by integrating two of our proposed cascades: one full body with another upper body detectors, we can reach higher accuracy than the standalone full body CoHOG-only in about 1/100 of its computational cost.