Hierarchical Feature Pooling with Structure Learning: A New Method for Pedestrian Detection

Objects such as pedestrians exhibit large intra-class variations, posing significant challenges for visual object detection. State-of-the-art part-based models explicitly model object deformations, but are limited in their ability to handle image variations incurred by other geometric and photometric changes, such as human pose, lighting, occlusions, and large appearance variations. In this paper, we propose a novel approach which uses a spatially-biased hierarchical scheme to map features into a high-dimensional space that better represents the rich set of object appearance and local deformation variations. We propose a new algorithm to jointly learn the classification function and feature pooling in this high-dimensional space, in a structured prediction setting. Our approach achieves the best detection performance on the INRIA pedestrian dataset.