  Detecting pedestrians, especially under heavy occlusions, is a challenging
computer vision problem with numerous real-world applications. This paper
introduces a novel approach, termed as PSC-Net, for occluded pedestrian
detection. The proposed PSC-Net contains a dedicated module that is designed to
explicitly capture both inter and intra-part co-occurrence information of
different pedestrian body parts through a Graph Convolutional Network (GCN).
Both inter and intra-part co-occurrence information contribute towards
improving the feature representation for handling varying level of occlusions,
ranging from partial to severe occlusions. Our PSC-Net exploits the topological
structure of pedestrian and does not require part-based annotations or
additional visible bounding-box (VBB) information to learn part spatial
co-occurrence. Comprehensive experiments are performed on two challenging
datasets: CityPersons and Caltech datasets. The proposed PSC-Net achieves
state-of-the-art detection performance on both. On the heavy occluded
(\textbf{HO}) set of CityPerosns test set, our PSC-Net obtains an absolute gain
of 4.0\% in terms of log-average miss rate over the state-of-the-art with same
backbone, input scale and without using additional VBB supervision. Further,
PSC-Net improves the state-of-the-art from 37.9 to 34.8 in terms of log-average
miss rate on Caltech (\textbf{HO}) test set.
