Pedestrian proposal generation using depth-aware scale estimation

In this work, we propose an efficient method that generates pedestrian proposals suitable for the autonomous vehicle. Our main intuition is that depth information provides an important cue to assign the scale of pedestrian proposals. Based on the observation that in a 3-D world coordinate the scales of pedestrians are almost similar, we formulate the scales of pedestrian patches by projecting 3-D models to an image plane with its corresponding depth. We also introduce a scale-aware binary description using both color and depth images. By using this descriptor, the regression models are trained to rank the pedestrian proposal candidates and adjust the proposal bounding boxes for an accurate localization. Our algorithm achieves significant performance gains compared to conventional proposal generation methods on the challenging KITTI dataset.