Abstract The current state of the art solutions for object detection describe each class by a set of models trained on discovered sub-classes (so called “components”), with each model itself composed of collections of interrelated parts (deformable models). These detectors build upon the now classic Histogram of Oriented Gradients+linear SVM combo. In this paper we revisit some of the core assumptions in HOG+SVM and show that by properly designing the feature pooling, feature selection, preprocessing, and training methods, it is possible to reach top quality, at least for pedestrian detections, using a single rigid component. We provide experiments for a large design space, that give insights into the design of classififiers, as well as relevant information for practitioners. Our best detector is fully feed-forward, has a single unifified architecture, uses only histograms of oriented gradients and colour information in monocular static images, and improves over 23 other methods on the INRIA, ETH and Caltech-USA datasets, reducing the average miss-rate over HOG+SVM by more than 30%