论文标题
在拥挤的场景中检测：一个建议，多个预测
Detection in Crowded Scenes: One Proposal, Multiple Predictions
论文作者
论文摘要
我们提出了一个简单但有效的基于提案的对象探测器，旨在检测拥挤的场景中被封闭的实例。我们方法的关键是让每个提案预测一组相关实例，而不是以前基于提案的框架中的一个相关实例。我们的检测器配备了新技术，例如EMD损耗和设置NMS，可以有效地处理检测高度重叠对象的困难。在FPN-RES50基线上，我们的检测器可以在挑战性的人口数据集和1.0 \％$ \ text {Mr}^{ - 2} $改进数据集中获得4.9 \％ap的收益，而没有铃铛和惠”。此外，在诸如可可（Coco）等杂乱无章的数据集上，我们的方法仍然可以实现中等的改进，这表明该方法对拥挤性是可靠的。代码和预训练模型将在https://github.com/megvii-model/crowddetection上发布。
We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes. The key of our approach is to let each proposal predict a set of correlated instances rather than a single one in previous proposal-based frameworks. Equipped with new techniques such as EMD Loss and Set NMS, our detector can effectively handle the difficulty of detecting highly overlapped objects. On a FPN-Res50 baseline, our detector can obtain 4.9\% AP gains on challenging CrowdHuman dataset and 1.0\% $\text{MR}^{-2}$ improvements on CityPersons dataset, without bells and whistles. Moreover, on less crowed datasets like COCO, our approach can still achieve moderate improvement, suggesting the proposed method is robust to crowdedness. Code and pre-trained models will be released at https://github.com/megvii-model/CrowdDetection.
