An Adaptively Attention-Driven Cascade Part-Based Graph Embedding Framework for UAV Object Re-Identification

Abstract

With the rapid development of unmanned aerial vehicles (UAVs), object re-identification (Re-ID) based on the UAV platforms has attracted increasing attention, and several excellent achievements have been shown in the traditional scenarios. However, object Re-ID in aerial imagery acquired from the UAVs is still a challenging task, which is mainly due to the reason that variable locations and diverse viewpoints in UAVs platform are always resulting in more appearance ambiguities among the intra-objects and inter-objects. To address the above issues, in this paper, we proposed an adaptively attention-driven cascade part-based graph embedding framework (AAD-CPGE) for UAV object Re-ID. The AAD-CPGE aims to optimally fuse node features and their topological characteristics on the multi-scale structured graphs of parts-based objects, and then adaptively learn the most correlated information for improving the object Re-ID performance. Specifically, we first executed GCNs on the parts-based cascade node feature graphs and topological feature graphs for acquiring multi-scale structured-graph feature representations. After that, we designed a self-attention-based module for adaptive node and topological features fusion on the constructed hierarchical parts-based graphs. Finally, these learning hybrid graph-structured features with the most correlation discriminative capability were applied for object Re-ID. Several experimental verifications on three widely used UAVs-based benchmark datasets were carried out, and comparison with some state-of-the-art object Re-ID approaches validated the effectiveness and benefits of our proposed AAD-CPGE Re-ID framework.