Pedestrian Detection with Unsupervised Multi-stage Feature Learning

This work reports state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model that uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information.

Abstract

Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model. The model uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information, and an unsupervised method based on convolutional sparse coding to pre-train the filters at each stage.