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The owner-member relationship between wheels and vehicles contributessignificantly to the 3D perception of vehicles, especially in embeddedenvironments. However, to leverage this relationship we must face two majorchallenges: i) Traditional IoU-based heuristics have difficulty handlingoccluded traffic congestion scenarios. ii) The effectiveness and applicabilityof the solution in a vehicle-mounted system is difficult. To address theseissues, we propose an innovative relationship prediction method, DeepWORD, bydesigning a graph convolutional network (GCN). Specifically, to improve theinformation richness, we use feature maps with local correlation as input tothe nodes. Subsequently, we introduce a graph attention network (GAT) todynamically correct the a priori estimation bias. Finally, we designed adataset as a large-scale benchmark which has annotated owner-memberrelationship, called WORD. In the experiments we learned that the proposedmethod achieved state-of-the-art accuracy and real-time performance. The WORDdataset is made publicly available athttps://github.com/NamespaceMain/ownermember-relationship-dataset.

Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System