论文标题
人群人群检测的可见功能指南
Visible Feature Guidance for Crowd Pedestrian Detection
论文作者
论文摘要
在人群场景中的重度阻塞和密集的聚会使行人发现成为一个具有挑战性的问题，因为根据无形的人类部分很难猜测一个精确的完整界限。为了破解这种螺母，我们提出了一种用于训练和推理的称为可见特征指南（VFG）的机制。在培训期间，我们采用可见功能来回归可见的边界框和完整边界框的同时输出。然后，我们仅在可见的边界框上执行NMS，以在推理中获得最佳的完整框。这种方式可以减轻NMS在人群场景中产生的无能影响，并更精确地使完整的框架。此外，为了在申请后的过程中简化特征关联，例如行人跟踪，我们将匈牙利算法应用于人类实例的零件。我们所提出的方法可以稳定地为两阶段和一阶段检测器带来的地图和AP50提高约2至3％。对于MR-2，尤其是更严格的IOU，它也更有效。关于CrowdHuman，Cityperson，Caltech和Kitti数据集的实验表明，可见的功能指南可以帮助探测器实现有希望的更好的表现。此外，零件协会为远见社区的人类提供了强大的基准。
Heavy occlusion and dense gathering in crowd scene make pedestrian detection become a challenging problem, because it's difficult to guess a precise full bounding box according to the invisible human part. To crack this nut, we propose a mechanism called Visible Feature Guidance (VFG) for both training and inference. During training, we adopt visible feature to regress the simultaneous outputs of visible bounding box and full bounding box. Then we perform NMS only on visible bounding boxes to achieve the best fitting full box in inference. This manner can alleviate the incapable influence brought by NMS in crowd scene and make full bounding box more precisely. Furthermore, in order to ease feature association in the post application process, such as pedestrian tracking, we apply Hungarian algorithm to associate parts for a human instance. Our proposed method can stably bring about 2~3% improvements in mAP and AP50 for both two-stage and one-stage detector. It's also more effective for MR-2 especially with the stricter IoU. Experiments on Crowdhuman, Cityperson, Caltech and KITTI datasets show that visible feature guidance can help detector achieve promisingly better performances. Moreover, parts association produces a strong benchmark on Crowdhuman for the vision community.
