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.

本文提出了一种名为 PS-RCNN 的人物检测方法，通过引入人形掩膜来解决高度遮挡的问题，使用两个 R-CNN 模块来检测图像中的不同部位的对象，以及 HRRA 模块以保留细节特征，相较于基准检测方法，在 CrowdHuman 数据集上提高了 4.49% 和 2.92% 的召回率和 AP 值。

通过主要目标抑制检测人群中的次要人员实例的 PS-RCNN

PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary  Object Suppression

We propose a method for multi-person detection and 2-D pose estimation that
achieves state-of-art results on the challenging COCO keypoints task. It is a
simple, yet powerful, top-down approach consisting of two stages.
In the first stage, we predict the location and scale of boxes which are
likely to contain people; for this we use the Faster RCNN detector. In the
second stage, we estimate the keypoints of the person potentially contained in
each proposed bounding box. For each keypoint type we predict dense heatmaps
and offsets using a fully convolutional ResNet. To combine these outputs we
introduce a novel aggregation procedure to obtain highly localized keypoint
predictions. We also use a novel form of keypoint-based Non-Maximum-Suppression
(NMS), instead of the cruder box-level NMS, and a novel form of keypoint-based
confidence score estimation, instead of box-level scoring.
Trained on COCO data alone, our final system achieves average precision of
0.649 on the COCO test-dev set and the 0.643 test-standard sets, outperforming
the winner of the 2016 COCO keypoints challenge and other recent state-of-art.
Further, by using additional in-house labeled data we obtain an even higher
average precision of 0.685 on the test-dev set and 0.673 on the test-standard
set, more than 5% absolute improvement compared to the previous best performing
method on the same dataset.

该论文提出了一种用于多人检测和二维姿势估计的方法，采用两个阶段的简单而强大的自上而下方法，结合使用 Faster RCNN 检测器、关键点基于非最大抑制（Non-Maximum-Suppression）和置信度计算，利用 COCO 数据集训练得到的该系统具有较高的平均精度和表现。