LLA: Loss-aware label assignment for dense pedestrian detection
Zheng Ge, Jianfeng Wang, Xin Huang, Songtao Liu, Osamu Yoshie
Abstract
Label assignment has been widely studied in general object detection because of its great impact on detectors’ performance. In the field of dense pedestrian detection, human bodies are often heavily entangled, making label assignment more important. However, none of the existing label assignment method focuses on crowd scenarios. Motivated by this, we propose Loss-aware Label Assignment (LLA) to boost the performance of pedestrian detectors in crowd scenarios. Concretely, 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. LLA is simple but effective. 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, becoming the first one-stage detector that surpasses Faster R-CNN in crowd scenarios.