  Detecting human bodies in highly crowded scenes is a challenging problem. Two
main reasons result in such a problem: 1). weak visual cues of heavily occluded
instances can hardly provide sufficient information for accurate detection; 2).
heavily occluded instances are easier to be suppressed by
Non-Maximum-Suppression (NMS). To address these two issues, we introduce a
variant of two-stage detectors called PS-RCNN. PS-RCNN first detects
slightly/none occluded objects by an R-CNN module (referred as P-RCNN), and
then suppress the detected instances by human-shaped masks so that the features
of heavily occluded instances can stand out. After that, PS-RCNN utilizes
another R-CNN module specialized in heavily occluded human detection (referred
as S-RCNN) to detect the rest missed objects by P-RCNN. Final results are the
ensemble of the outputs from these two R-CNNs. Moreover, we introduce a High
Resolution RoI Align (HRRA) module to retain as much of fine-grained features
of visible parts of the heavily occluded humans as possible. Our PS-RCNN
significantly improves recall and AP by 4.49% and 2.92% respectively on
CrowdHuman, compared to the baseline. Similar improvements on Widerperson are
also achieved by the PS-RCNN.
