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

• URL: http://arxiv.org/abs/2201.12099v1
• Date: Fri, 28 Jan 2022 13:12:27 GMT
• Title: Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System
• Authors: Zizhang Wu, Jason Wang, Tianhao Xu, Fan Wang
• Abstract summary: We propose an innovative relationship prediction method, DeepWORD, by designing a graph convolutional network (GCN) In the experiments we learned that the proposed method achieved state-of-the-art accuracy and real-time performance.
• Score: 9.665475078766017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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