HSS-GCN: A Hierarchical Spatial Structural Graph Convolutional Network for Vehicle Re-identification

Vehicle re-identification (Re-ID) is the task aiming to identify the same vehicle from images captured by different cameras. Recent years have seen various appearance-based approaches focusing only on global features or exploring local features to obtain more subtle details which can alleviate the subtle inter-instance problem. However, few emphasize the spatial geometrical structure relationship among local regions or between the global region and local regions. To explore above-mentioned spatial structure relationship, this paper proposes a hierarchical spatial structural graph convolutional network (HSS-GCN) for vehicle Re-ID, in which we firstly construct a hierarchical spatial structural graph with the global region and local regions as nodes and a two-hierarchy relationship as edges, and later learning discriminative structure features with a GCN module under the constraints of metric learning. To augment the performance of our proposed network, we jointly combine the classification loss with metric learning loss. Extensive experiments conducted on the public VehicleID and VeRi-776 datasets validate the effectiveness of our approach in comparison with recent works.