  Pedestrian detection has significantly progressed in recent years, thanks to
the development of DNNs. However, detection performance at occluded scenes is
still far from satisfactory, as occlusion increases the intra-class variance of
pedestrians, hindering the model from finding an accurate classification
boundary between pedestrians and background clutters. From the perspective of
reducing intra-class variance, we propose to complete features for occluded
regions so as to align the features of pedestrians across different occlusion
patterns. An important premise for feature completion is to locate occluded
regions. From our analysis, channel features of different pedestrian proposals
only show high correlation values at visible parts and thus feature
correlations can be used to model occlusion patterns. In order to narrow down
the gap between completed features and real fully visible ones, we propose an
adversarial learning method, which completes occluded features with a generator
such that they can hardly be distinguished by the discriminator from real fully
visible features. We report experimental results on the CityPersons, Caltech
and CrowdHuman datasets. On CityPersons, we show significant improvements over
five different baseline detectors, especially on the heavy occlusion subset.
Furthermore, we show that our proposed method FeatComp++ achieves
state-of-the-art results on all the above three datasets without relying on
extra cues.
