# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
###### 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 spatial and channel dimensions 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. In addition, DMAM can be easily plugged into any depth of the backbone network to improve vehicle recognition. The experimental results show that the proposed structure performs better than the representative method in the UAV-based vehicle ReID. Our code and models will be published publicly. Keywords:dual mixing attention; UAV re-identification; deep learning +
Footnote †: journal: Accepted: 9 October 2023  
captured by UAV and those acquired through stationary cameras. The ReID challenge regarding UAV imagery introduces unique complexities stemming from intricate shooting angles, occlusions, limited discriminative power of top-down features, and substantial variations in vehicle scales. It is worth mentioning that traditional vehicle ReID methods, primarily designed for stationary cameras, face challenges in delivering optimal performance when adapted to the domain of UAV-based ReID. Firstly, the shooting angle of UAVs is complex. UAVs can shoot at different positions and angles, and the camera's viewpoint will change accordingly. This viewpoint change may cause the same object or scene to have different appearances and characteristics in different images. Second, the UAV can overlook or squint at a target or scene at different angles, resulting in viewpoint changes in the image. This viewpoint change may cause deformation or occlusion of the target shape, thus causing difficulties for feature extraction. To solve the above problems, it is necessary to add a mechanism [1, 13, 14, 15] that can extract more detailed features when ReID extracts features to deal with the challenges brought by the drone perspective. The change in the UAV viewpoint makes the feature extraction algorithm need a certain robustness, which can correctly identify and describe the target in the case of significant changes in the viewpoint. The difference in the UAV view angle makes the feature extraction algorithm need to have the ability to adapt to shape changes and occlusions to improve the feature reliability and robustness in different views. In recent years, the attention mechanism has gained significant popularity across multiple domains of deep convolutional neural networks. Its fundamental concept revolves around identifying the most crucial information for a given target task from a vast volume of available data. The attention mechanism selectively focuses on the image's different regions or feature channels to improve the model's attention and perception ability for crucial visual content. In the context of UAV-based vehicle ReID, the attention mechanism enables the model to enhance its perception capabilities by selectively highlighting the vehicle's specific regions or feature channels. However, most attention mechanisms [15, 16, 17, 18] focus on extracting features only from channels or spaces.