  Label assignment has been widely studied in general object detection because
of its great impact on detectors' performance. However, none of these works
focus on label assignment in dense pedestrian detection. In this paper, we
propose a simple yet effective assigning strategy called Loss-aware Label
Assignment (LLA) to boost the performance of pedestrian detectors in crowd
scenarios. LLA first calculates classification (cls) and regression (reg)
losses between each anchor and ground-truth (GT) pair. A joint loss is then
defined as the weighted summation of cls and reg losses as the assigning
indicator. Finally, anchors with top K minimum joint losses for a certain GT
box are assigned as its positive anchors. Anchors that are not assigned to any
GT box are considered negative. Loss-aware label assignment is based on an
observation that anchors with lower joint loss usually contain richer semantic
information and thus can better represent their corresponding GT boxes.
Experiments on CrowdHuman and CityPersons show that such a simple label
assigning strategy can boost MR by 9.53% and 5.47% on two famous one-stage
detectors - RetinaNet and FCOS, respectively, demonstrating the effectiveness
of LLA.
