  Detecting human in a crowd is a challenging problem due to the uncertainties
of occlusion patterns. In this paper, we propose to handle the crowd occlusion
problem in human detection by leveraging the head part. Double Anchor RPN is
developed to capture body and head parts in pairs. A proposal crossover
strategy is introduced to generate high-quality proposals for both parts as a
training augmentation. Features of coupled proposals are then aggregated
efficiently to exploit the inherent relationship. Finally, a Joint NMS module
is developed for robust post-processing. The proposed framework, called Double
Anchor R-CNN, is able to detect the body and head for each person
simultaneously in crowded scenarios. State-of-the-art results are reported on
challenging human detection datasets. Our model yields log-average miss rates
(MR) of 51.79pp on CrowdHuman, 55.01pp on COCOPersons~(crowded sub-dataset) and
40.02pp on CrowdPose~(crowded sub-dataset), which outperforms previous baseline
detectors by 3.57pp, 3.82pp, and 4.24pp, respectively. We hope our simple and
effective approach will serve as a solid baseline and help ease future research
in crowded human detection.
