Crowdassign: A Label Assignment Scheme for Pedestrian Detection in Crowded Scenes

Pedestrian detection in crowded scenes is a challenging problem. To avoid missing targets in a crowd, a high non-maximum suppression (NMS) threshold is necessary. However, a high NMS threshold may lead to a large number of false positives (FP), which results in serious degradation of pedestrian detection performance. In this paper, we propose a novel label assignment scheme for one-stage detectors, called CrowdAssign, which focuses on FP suppression. Firstly, we analyze the sources of FPs and classify them into different categories. Secondly, we examine the cause of different types of FP and develop a dynamic classification label adjustment strategy, which aims to reduce the confidence of the positive samples with high risk of being FPs. With our robust label assignment scheme, a single FCOS-ResNet-50 detector can reach $45.0 \ M R^{-2}$ on CrowdHuman under 1x schedule and outperforms state-of-the-art label assignment methods. Experiments on Citypersons demonstrate the desired generalization ability of our algorithm.