  Pedestrian detection in the wild remains a challenging problem especially for
scenes containing serious occlusion. In this paper, we propose a novel feature
learning method in the deep learning framework, referred to as Feature
Calibration Network (FC-Net), to adaptively detect pedestrians under various
occlusions. FC-Net is based on the observation that the visible parts of
pedestrians are selective and decisive for detection, and is implemented as a
self-paced feature learning framework with a self-activation (SA) module and a
feature calibration (FC) module. In a new self-activated manner, FC-Net learns
features which highlight the visible parts and suppress the occluded parts of
pedestrians. The SA module estimates pedestrian activation maps by reusing
classifier weights, without any additional parameter involved, therefore
resulting in an extremely parsimony model to reinforce the semantics of
features, while the FC module calibrates the convolutional features for
adaptive pedestrian representation in both pixel-wise and region-based ways.
Experiments on CityPersons and Caltech datasets demonstrate that FC-Net
improves detection performance on occluded pedestrians up to 10% while
maintaining excellent performance on non-occluded instances.
