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A Novely Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
Version 1
: Received: 15 September 2023 / Approved: 15 September 2023 / Online: 18 September 2023 (07:30:54 CEST)
A peer-reviewed article of this Preprint also exists.
Yin, W.; Peng, Y.; Ye, Z.; Liu, W. A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification. Appl. Sci. 2023, 13, 11651. Yin, W.; Peng, Y.; Ye, Z.; Liu, W. A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification. Appl. Sci. 2023, 13, 11651.
Abstract
Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of Unmanned Aerial Vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top-down features, and significant changes in vehicle scales. To address this, we propose a novel Dual Mixing Attention Network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play Dual Mixing Attention Module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of Spatial Mixing Attention (SMA) and Channel Mixing Attention (CMA). First, the original feature is divided according to the dimensions of spatial and channel to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. Moreover, the DMAM can be readily inserted into backbone networks at any depth to improve vehicle discrimination. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task. Our code and models will be publicly released.
Keywords
dual mixing attention; UAV re-identification; deep learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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