论文标题
在鱼眼相机系统中检测所有者与图形卷积网络的关系
Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System
论文作者
论文摘要
车轮和车辆之间的所有者成员关系对车辆的3D感知产生了重大贡献，尤其是在嵌入式环境中。但是，要利用这种关系，我们必须面临两个主要挑战：i）基于IOU的传统启发式方法难以处理封闭的交通拥堵场景。 ii）很难在车辆安装系统中解决方案的有效性和适用性。为了解决这些问题，我们通过设计图形卷积网络（GCN）提出了一种创新的关系预测方法。具体来说，为了提高信息丰富性，我们使用具有局部相关性的特征图作为与节点的输入。随后，我们引入图形注意网络（GAT），以动态纠正先验估计偏置。最后，我们将数据集设计为一个大规模的基准，该基准具有注释所有者的关系，称为Word。在实验中，我们了解到所提出的方法达到了最先进的准确性和实时性能。该单词数据集可在https://github.com/namespacemain/ownermember-relationship-dataset上公开获得。
The owner-member relationship between wheels and vehicles contributes significantly to the 3D perception of vehicles, especially in embedded environments. However, to leverage this relationship we must face two major challenges: i) Traditional IoU-based heuristics have difficulty handling occluded traffic congestion scenarios. ii) The effectiveness and applicability of the solution in a vehicle-mounted system is difficult. To address these issues, we propose an innovative relationship prediction method, DeepWORD, by designing a graph convolutional network (GCN). Specifically, to improve the information richness, we use feature maps with local correlation as input to the nodes. Subsequently, we introduce a graph attention network (GAT) to dynamically correct the a priori estimation bias. Finally, we designed a dataset as a large-scale benchmark which has annotated owner-member relationship, called WORD. In the experiments we learned that the proposed method achieved state-of-the-art accuracy and real-time performance. The WORD dataset is made publicly available at https://github.com/NamespaceMain/ownermember-relationship-dataset.
