The Discriminative Generalized Hough Transform as a Proposal Generator for a Deep Network in Automatic Pedestrian Localization

Pedestrian detection is one of the most essential and still challenging tasks in computer vision. Among traditional feature- or model-based techniques (e.g., histograms of oriented gradients, deformable part models etc.), deep convolutional networks have recently been applied and significantly advanced the state-of-the-art. While earlier versions (e.g., Fast-RCNN) rely on an explicit proposal generation step, this has been integrated into the deep network pipeline in recent approaches. It is, however, not fully clear if this yields the most efficient way to handle large ranges of object variability (e.g., object size), especially if the amount of training data covering the variability range is limited. We propose an efficient pedestrian detection framework consisting of a proposal generation step based on the Discriminative Generalized Hough Transform and a rejection step based on a deep convolutional network. With a few hundred proposals per (2D) image, our framework achieves state-of-the-art performance compared to traditional approaches on several investigated databases. In this work, we analyze in detail the impact of different components of our framework.